System and method for annotating recorded information from contacts to contact center

ABSTRACT

A system for forming a database indexed with content-based parameters, corresponding to recordings of contacts to a contact center, includes an analysis unit, a mining unit, and a correlation unit. The analysis unit performs automated-portion analysis techniques to ascertain predetermined events occurring in recordings of automated portions of the contacts to the contact center. The mining unit performs audio mining of recordings of agent portions of the contacts to the contact center. The correlation unit correlates an event occurring in an agent portion of the recordings with a corresponding one of a plurality of content-based parameters selected to be used as indices of the database, and makes an entry in the database based on the correlated event and the corresponding content-based parameter.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Appln. No.60/273,710, filed Mar. 5, 2001, and U.S. Provisional Appln. No.60/276,266, filed Mar. 15, 2001, which are each hereby incorporatedherein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to systems and methods for assessing callcenter performance and, in particular, to systems and methods that allowthe performance and current and potential levels of automation of a callcenter to be quantified.

2. Discussion of the Related Art

Telephone user interfaces are the most widespread class ofhuman-computer interfaces. Introduced more than a decade ago, touch-toneinteractive voice response (IVR) systems were adopted enthusiasticallyin many call-centers and promised to provide customer serviceefficiently. However, calling customers (callers) have exhibitedantipathy towards touch-tone IVR systems, viewing them as difficult touse. Further aggravating the callers is the fact that they often endurelong waiting times before they can speak to live agents. This dichotomyis not surprising considering that most call-centers focus on minimizingoperating costs and that usability and its impact on call-centeroperations are poorly understood.

Since touch-tone IVR systems have been deployed for more than a decade,a significant body of know-how on IVR systems has accumulated in theindustry. Except for recent attempts to define a style guide for(telephone) speech applications, as in Balentine, B. and D. P. Morgan,How to Build A Speech Recognition Application, 1999, San Ramon, Calif.,Enterprise Integration Group, and to introduce universal commands inspeech-enabled IVR systems, as in Cohen, M., Universal Command forTelephony—Based Spoken Language Systems, SIG-CHI Bulletin, 2000, 32(2),pp. 25-30, this body of knowledge is not well documented. The prevalenceof usability problems in deployed IVR systems suggests that designinggood telephone interfaces is difficult and usability engineering methodsfor telephone interfaces are not well developed.

Another area of related work is research on spoken it dialog systems, animportant application of speech recognition technology. Spoken dialogsystems allow the caller to communicate with a system in a spokendialog, not necessarily over the telephone. Many research articles onspoken dialog systems have been published, e.g., Stallard, D.,Talk'N'Travel: A Conversational System for Air Travel Planning, inApplied Natural Language Processing ANLP, 2000, Seattle, Wash.; Peckham,J., A new generation of spoken language systems: recent results andlessons from the SUNDIAL project, in European Conference on SpeechCommunication and Technology EUROSPEECH, 1993, Berlin (Germany):European Speech Communication Association; Levin, E. and R. Pieraccini,CHRONUS: The Next Generation, in ARPA Workshop on Spoken LanguageTechnology, 1995, Austin (TX): Morgan Kaufmann Publishers, Inc.;Bennacef, S., et al., Dialog in the RIALTEL telephone-based system, inInternational Conference on Spoken Language Systems ICSLP, 1996,Philadelphia, Pa.; and Lee, C. H., et al., On Natural Language CallRouting, in Speech Communications, 2000, 31, pp. 309-320.

Previous work on spoken-dialog system evaluation focused on quantifyingthe performance of the underlying technologies, e.g., Chang, H., A.Smith, and G. Vysotsky, An automated performance evaluation system forspeech recognizers used in the telephone network, in InternationalConference on World Prosperity Through Communications, 1989; andPallett, D. S., et al., 1993 Benchmark Tests for the ARPA SpokenLanguage Program, in ARPA Workshop on Spoken Language Technology, 1994,Princeton (NJ): Morgan Kaufmann Publishers, Inc.

Some studies have evaluated the usability of telephone interfaces basedon task completion rates and post-experimental questionnaires, e.g.,Edwards, K., et al., Evaluating Commercial Speech Recognition and DTMFTechnology for Automated Telephone Banking Services, in IEEE Colloquiumon Advances in Interactive Voice Technologies for TelecommunicationServices, 1997. More recently, PARADISE was introduced as a “consistentintegrative framework for evaluation” of spoken language systems, asdescribed in Walker, M. A., et al., PARADISE: A Framework for evaluatingspoken dialogue agents, in 35th Annual Meeting of the Association ofComputational Linguistics, 1997, Madrid: Morgan Kaufmann Publishers,Inc. Basically, PARADISE provides a method to identify measures thatpredict user satisfaction well, from the large set of measures that havebeen used in the field. However, this work does not address the cost forthe call center, nor does it provide any guidance for telephoneinterface redesign.

SUMMARY OF THE INVENTION

The present invention overcomes the deficiencies of earlier systems bypresenting a methodology for evaluating both usability andcost-effectiveness of telephone user interfaces. The assessmentmethodology of the present invention, and the various inventivetechniques utilized within that methodology, provide usabilitypractitioners with tools to identify and quantify usability problems intelephone interfaces, and provide call-center managers with the businessjustification for the cost of usability-improvement engineering.

The present invention relates to a system for annotating recordedinformation from calls to a call center, to enable assessment of thecall center. Of course, although the descriptions herein relate to callcenters that process telephone calls, other types of contact centersother than call centers are within the realm of the present invention,including (but not limited to) Internet-based contact centers wherecustomers contact a company's contact center via the Internet usingknown ways for transmitting messages and information via the Internet.

According to one aspect of the present invention, there is provided amethod for forming a database indexed with content-based parameterscorresponding to recordings of contacts to a contact center. The contactcenter is operable to present a contacting party with a contact thatincludes an automated portion corresponding to interaction with anautomated response interface and, at the contacting party's option, anagent portion corresponding to interaction with an agent. Eventsoccurring in the recordings of automated portions are ascertained bypreviously executed automated-portion analysis techniques. The methodcomprises the steps of: selecting content-based parameters to be used asindices in a database; analyzing results of the automated-portionanalysis techniques to detect occurrence of events corresponding to theselected parameters; analyzing one or more agent portions of recordingsof contacts to ascertain events occurring therein; correlating eventsoccurring in the one or more agent portions of the recordings withcorresponding ones of the selected parameters; and making entries in thedatabase based on the selected parameters and the correlated events.

According to another aspect of the present invention, there is provideda system for forming a database indexed with content-based parameterscorresponding to recordings of contacts to a contact center. The contactcenter is operable to present a contacting party with a contact thatincludes an automated portion corresponding to interaction with anautomated response interface and, at the contacting party's option, anagent portion corresponding to interaction with an agent. The systemcomprises: analysis means operable to perform automated-portion analysistechniques to ascertain predetermined events occurring in recordings ofautomated portions of contacts to a contact center; mining meansoperable to perform audio mining of recordings of agent portions of thecontacts to the contact center; and correlation means. The mining meansincludes: a speech/non-speech detector operable to identify speech andnon-speech events in an audio recording; a speaker-change detectoroperable to identify speaker turns in an audio recording; a speechrecognizer operable to output a sequence of words for each speaker turnidentified by the speaker-change detector; a topic detector operable todetermine a topic of an audio recording; and a named-entity detectoroperable to identify speech pertaining to an entity named in an audiorecording. The correlation means correlates an event occurring in anagent portion of the recordings with a corresponding one of a pluralityof content-based parameters selected to be used as indices of adatabase, and makes an entry in the database based on the correlatedevent and the corresponding content-based parameter.

According to yet another aspect of the present invention, there isprovided a computer program product embodying a program for implementinga method for forming a database indexed with content-based parameterscorresponding to recordings of contacts to a contact center. The contactcenter is operable to present a contacting party with a contact thatincludes an automated portion corresponding to interaction with anautomated response interface and, at the contacting party's option, anagent portion corresponding to interaction with an agent. Eventsoccurring in the recordings of automated portions are ascertained bypreviously executed automated-portion analysis techniques. The computerprogram product comprises code for: selecting content-based parametersto be used as indices in a database; analyzing results of theautomated-portion analysis techniques to detect occurrence of eventscorresponding to the selected parameters; analyzing one or more agentportions of recordings of contacts to ascertain events occurringtherein; correlating events occurring in the one or more agent portionsof the recordings with corresponding ones of the selected parameters;and making entries in the database based on the selected parameters andthe correlated events.

These and other objects, features, and advantages of the presentinvention will be apparent from the following description of thepreferred embodiments considered in conjunction with the correspondingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating components of a completesystem for call center assessment, in accordance with preferredembodiments of the present invention;

FIG. 1A is a chart describing a workflow of an assessment in accordancewith the present invention;

FIGS. 2A-2C illustrate three options for recording incoming calls to acall center;

FIG. 3A is an exemplary IVR flow chart that maps out possible paths acaller can take while interacting with an IVR system;

FIG. 3B, which is a composite of FIGS. 3C-1 and 3C-2, shows a moredetailed flow chart for an IVR system;

FIG. 3D is a schematic diagram of an IVR system;

FIG. 3E is a chart showing typical data collected for a call.

FIG. 3F shows an IVR log that includes an area for identifying thecaller;

FIG. 4 is a diagram that illustrates a complete process of an IVR systemanalysis, according to a preferred embodiment of the present invention;

FIG. 5 is a diagram illustrating an exemplary architecture for acomputer-based indexing system;

FIG. 6A is a flow chart illustrating a mini assessment process,according to the present invention;

FIG. 6B, which is a composite of FIGS. 6B-1 through 6B-4, shows anexample of a coding sheet used in mini assessment;

FIG. 6C, which is a composite of FIGS. 6C-1 through 6C-4, shows anexample of tabulated results from an analysis of calls to a call center;

FIG. 6D is an example of an analysis report produced based on thetabulated results illustrated in FIG. 6B;

FIG. 7 is a diagram illustrating a process for measuring existingautomation levels;

FIG. 8 is an example of a spreadsheet generated to assist in IVR systemautomation analysis;

FIG. 9 is a chart showing an example of standard benefit assumptions;

FIG. 10 is a chart for calculating an automation benefit of a callcenter, in accordance with an embodiment of the present invention;

FIG. 11 shows a chart useful for calculating an upper bound on callcenter automation;

FIG. 12 is a bar chart for comparing upper bounds of automation withexisting levels of automation in a call center;

FIG. 13 is a chart illustrating potential savings of agent time in acall center;

FIG. 14 is an exemplary user-path diagram, in accordance with apreferred embodiment of the present invention;

FIG. 15 is a flow diagram illustrating a process for generating auser-path diagram, in accordance with the present invention;

FIG. 15A is a diagram that illustrates how to read a user path diagram;

FIG. 16 shows an exemplary confusion matrix, developed in accordancewith the present invention;

FIG. 16A is a flow chart illustrating a method of manipulating call datafiles so that they may be visualized in a confusion matrix;

FIG. 17 is an exemplary excerpt from a .sum file;

FIG. 18 is an exemplary life-of-call diagram;

FIG. 19 is a chart comparing cost savings in three different IVRsystems;

FIG. 19A illustrates projected benefits for a touch-tone redesign versusa speech-enable call center; and

FIG. 20 is a diagram illustrating a technique for monitoring IVR systemperformance.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention provides techniques for assessing interactivevoice response (IVR) systems. The IVR system assessment techniques ofthe present invention provide functionality for evaluating theperformance of commercial call centers in detail and can assist inproviding concrete recommendations on how a call center IVR system canbe improved. As part of this process, the IVR system assessment aspracticed according to the present invention also quantifies thepotential for improvement, in particular, the maximum operational (cost)savings a re-engineering effort could deliver. Advantageous results ofan IVR system assessment include a detailed IVR system usabilityanalysis, a set of recommendations for redesigning the system, and abusiness case for various redesign options.

I. Overview of Enumerated Techniques

Several new techniques are described below in the context of assessmentof call-center performance, in relation to call-center automation ingeneral and to IVR systems in particular. For ease of reference, thesetechniques will be identified by reference numbers, as shown in FIG. 1,and may be referred to as such throughout the specification. Thetechniques include: routing analysis 1, on-site and off-site end-to-endcall recording 2, IVR logging 3, IVR system performance monitoring 4,mini assessment 5, IVR system analysis and prompt inference 6, callannotation and information retrieval 7, automation analysis 8, user-pathdiagram 9, and life-of-call diagram 10.

Initially, the above techniques will briefly be discussed in terms ofhow they relate to the overall assessment method and to one another.According to a preferred embodiment, the techniques enumerated above fitinto the overall assessment method of the present invention as follows.

The assessment technique proceeds in two major phases. In the firstphase, data from live calls is collected and processed into a formatsuitable for further analyses, that is, a database of complete callevent-sequences for every call. The event sequence for every call shouldencompass both the IVR system-caller dialog and agent-caller dialog. Thelatter is included if such a dialog occurred during the call.

FIG. 1

In the second phase, based on the call event-sequence database, callerbehavior is analyzed from different aspects and summary statistics aregenerated. FIG. 1 illustrates a complete process for an IVR systemassessment and how the various enumerated techniques fit into apreferred embodiment of the assessment method and system of the presentinvention. Roughly speaking, the upper part of FIG. 1 illustratesvarious advantageous methods for obtaining a database of callevent-sequences, and the lower part shows various assessment analysistechniques in accordance with the invention.

The capture of call event-sequences will be discussed next. The onlycomplete record of user and system behavior in telephone user interfacesare complete calls. Therefore, any comprehensive assessment of telephoneuser interfaces must be based on complete records of calls, which may bestored in the form of a database of call event-sequences, shown as acentral object in FIG. 1. A call typically begins with a dialog with anautomated (IVR) system, called the IVR system-caller dialog, which maybe followed by a dialog with a live agent, called the agent-callerdialog.

Three of the enumerated techniques are used to collect data fromcomplete calls: IVR logging 3, call recording 2, and (on-line) callmonitoring 5. First, in IVR logging 3, the IVR system itself provides acomplete record (log) of system prompts and user responses. To obtain anIVR log, the IVR program is configured to write an event log at everysignificant state, as the call passes through the call-flow logic of theprogram. All log entries for a specific call constitute the completeevent sequence for the IVR system-caller dialog of that call, whichsequence can be stored in the call event-sequence database. The IVRlogging technique is discussed in more detail below.

Second, end-to-end recordings of calls are obtained by passing incomingcalls through specialized recording hardware, which stores the audiosignal for a complete call into a file. Methods for obtaining end-to-endcall recordings in accordance with the present invention are describedbelow in the detailed discussion of call recording 2. Recordings ofcomplete calls represent a large amount of data that is difficult toanalyze in its raw form. For example, one hour of recording on onechannel at 8 kHz with an 8-bit resolution corresponds roughly to 30Mbytes of audio data. To make the analysis of call data efficient, callrecordings are transformed into complete IVR event sequences using IVRsystem analysis and prompt inference 6, as described below in thedetailed description of that technique.

Finally, instead of recording, a call can also be monitored manually,resulting in a rough, on-line annotation of the events of a completecall, including both the IVR system-caller dialog and the caller-agentdialog. This method is used in mini assessment 5, to be described indetail below. The main advantage of a mini-assessment over the two datacollection methods discussed above is its low cost: instead of loggingor recording and analyzing thousands of calls, only a few hundred callsare monitored and annotated manually, thus trading cost for accuracy.

Call analyses, assessment diagnostics, and customer relationshipmanagement (CRM) applications will be discussed next. Once eventsequences have been obtained for many calls, these call records can beused in three main ways: to (constantly) monitor IVR system performance,to conduct a detailed IVR system assessment, and to annotate andretrieve information, e.g., for customer relationship management (CRM)purposes.

First, in IVR system performance monitoring 4, the performance of an IVRsystem is monitored constantly using a few key performance statistics.These performance statistics are obtained directly from IVR logs in afully automated fashion, allowing monitoring to be conducted on anon-going basis. Suitable performance statistics include IVR systemautomation, detailed measures thereof, such as rate of successfulcapture of caller ID or successful routing or delivery of information tothe caller, or total IVR system benefit. Techniques for gathering thesestatistics are described in more detail below in relation to thetechnique for automation analysis 8.

Second, IVR system assessment techniques are used to analyze the callrecords in several ways to obtain detailed diagnostics for IVR systemusability and efficiency. In one such technique, a user-path diagram 9is produced that represents complete IVR event sequences visually in astate-transition diagram, which is annotated with the IVR exitconditions and the levels of automation achieved for every call. Auser-path diagram essentially shows where callers went in the IVR system(what choices they made), whether they received useful information inthe IVR system, where they abandoned the IVR system by hanging up, andif and where in the IVR program they were transferred to an agent. Anexample of a user-path diagram technique in accordance with a preferredembodiment will be described in detail below.

Routing analysis 1 is a technique that allows the performance of IVRrouting to be visualized by graphically comparing choices made in IVR tothe true reason for the call (or “call type”), as determined byannotations of agent-caller interactions. A confusion matrix, to bediscussed below, is an example of such a graphical comparison.

Automation analysis 8 is a technique that quantifies the benefit of theexisting IVR system as well as the potential for increasing automation.The benefit is calculated in terms of agent seconds (averaged across allcalls), i.e., agent time saved by completing transactions in the IVRsystem that otherwise would have to be handled by an agent. Theautomation analysis 8 takes into account the benefit for routing callersto the correct place in the IVR system, which is measured in the routinganalysis 1.

Finally, a life-of-call diagram 10 is a graphical technique that allowsvital timing information for broad sections of a call, such asIVR-caller dialog, hold sections, and caller-agent dialogs, to bevisualized. The timing of these sections is categorized by differentcall types, such as calls abandoned in the IVR system or while on holdfor an agent, calls fully served in the IVR system, and calls handled byvarious kinds of specialist agents.

A mini assessment 5 performs most of the analyses described above basedon manual annotations of a limited number of calls, which are monitoredon-line. Thus, a mini assessment 5 can be viewed as a version of an IVRsystem assessment that delivers less accurate results but at a lowercost.

As shown by various arrows in FIG. 1, several of the IVR systemassessment analysis techniques shown in that figure, in particulartechniques 1, 8, and 10, require specific annotations of caller-agentdialogs. Techniques for gathering such annotations are described belowas part of a more general method of annotating calls.

Finally, calls can be annotated with semantic events, and a database ofcall event-sequences enriched with semantic information can be accessedusing information retrieval techniques such as annotation 7. Calls canbe annotated with semantic information using at least three methods: byon-line monitoring (as in the mini-assessment), by manually annotatingrecorded calls, or by automatically annotating recorded calls. Semanticannotations can be limited to inputs required by an assessment, such asthe true reason for a call (call type) and transactions or exchanges ofinformation that are provided by the live agent but that could have beenobtained in an automated (IVR) system. Once such a semantic calldatabase has been developed, standard information retrieval techniquescan be employed to access it. A detailed discussion of the specifictechniques of the overall assessment system will next be described.

FIG. 1A

The following describes the workflow of an assessment according to thepreferred embodiment. The workflow is described with reference to FIG.1A.

First, at step 1000 the recording method must be determined, i.e., howcalls are being recorded end-to-end. The IVR platform employed in thecall center determines which recording method (as described above) isfeasible. Preferred are IVR platforms with a remote service-observationfeature, because it makes end-to-end recording possible with the leastamount of configuration required.

At the same stage in the workflow as determining the recording method,call annotation 7 and prompt inference 6 are configured. To configureannotation 7, at step 1002 a coding sheet is developed that determineswhat constitutes “significant” events in the agent-caller dialog for thecall center under investigation. As part of the configuration of promptinference 6, at step 1003 a non-deterministic finite-state automaton isdefined that models the call-flow (IVR) logic. In a preferredembodiment, the finite-state automaton is defined using a flat text filecalled a “call flow specification” or “.cfs” file. Once a .cfs file isestablished, a set of prompts that are to be detected (rather thaninferred) can be determined, as will be described in more detail infra.

At step 1004, end-to-end recordings of calls are made. Once end-to-endrecordings of calls are available, call annotation 7 begins, at step1005, and the various steps of prompt inference 6 can be configured. Ina preferred embodiment, annotation 7 is performed manually by humanannotators. To complete configuration of prompt inference 6, at step1006 sample prompts are obtained from sample recordings for each promptthat is to be detected. The set of prompts that are to be detected havebeen determined earlier, based on the call-flow specification or .cfsfile. Also, once recordings are available, at step 1007 a DTMF(dual-tone multi-frequency) detector is run automatically to obtainsequences of caller DTMF inputs for every recorded call. Furthermore,computer programs that process thousands of recordings are configured toperform the steps of IVR system analysis 6, i.e., to configure callsplitting at step 1009, to configure prompt detection at step 1008, andto configure prompt inference at step 1011. This is accomplished byediting standard configuration files to suit current needs.

With the completion of IVR system analysis 6 at steps 1007 and 1009-1013and call annotation 7 at step 1005, a sequence of significant events isavailable for every call, including both the IVR system-caller andagent-caller dialogs, as applicable. Further processes to be discussedin detail infra compile this event-sequence data to produce a user-pathdiagram 9, a routing analysis 1, an automation analysis 8, and alife-of-call diagram 10.

In a preferred embodiment, the user-path diagram 9 is generated usingonly IVR event-sequence data. At step 1021 several computer scriptssuccessively compile the event-sequence data into a file that containsthe IVR events for each call in a single line, called a .sum file, fillin a two-way matrix that contains IVR system state-transitioninformation, called a .trans file, optionally collapse thestate-transition information by clustering states, and transform thestate transition information into a rough layout as a tree, similar tothe examples of user-path diagrams discussed infra. At step 1017 ananalyst determines which states of the call flow should be clustered andrepresented as a single state in the user-path diagram, and a file mustindicate the position and sizing of the various states to be displayedin the user-path diagram. The rough layout of the user-path diagram thusobtained is then loaded into a visualization tool (e.g., Microsoft™Vision™) and at step 1025 the layout is cleaned-up manually.

At step 1019, routing analysis is performed using computer scripts thatextract, for every annotated call, the IVR exit point from the IVRevent-sequence and the call type from the agent-caller event sequence,and fill in a two-way routing matrix with counts as described infra. Toconfigure these scripts, at step 1015 a file is generated that definesthe different IVR exit points and call types, and how they appear in theIVR system-caller and agent-caller event sequences, respectively. Theconfigured scripts are cleaned up manually using graphing programsavailable in standard spreadsheets (e.g., Microsoft™ Excel™).

Automation analysis 8 is performed based on the IVR event-sequence dataand results of the routing analysis 1. At step 1020 computer scriptscompile the IVR event-sequences into tables that collapse calls intovarious automation profiles, and accumulate count statistics for eachprofile. These call-profile count-statistics are read into a standardspreadsheet program. The spreadsheets are configured at step 1016 toaccount for misrouting, as described infra. Then, spreadsheetcalculations produce a IVR system automation report and estimate a totalIVR system benefit.

The process for creating a life-of-call diagram 10 is similar. First, acall-segment timing analysis is configured at step 1014 by indicatingthe different call types that are to be distinguished and which callsegments have been annotated in calls. Next, at step 1018 computerscripts compile timing information based on annotations of every call,and calculate average timings across various call types. The output fromthe computer scripts, in table form, is read into a standard spreadsheetprogram that is configured at step 1022 to produce bar charts for thelife-of-call diagram 10, as described infra.

Once all analyses have been completed, at step 1024 an analyst reviewsall the data to identify specific problems in the call flow and to inferrecommendations for call-flow redesign.

II. Detailed Description of Assessment Techniques

As outlined above, the present invention provides various techniquesthat form part of an assessment methodology operable to evaluate bothcost effectiveness and usability of telephone interfaces based on adetailed analysis of end-to-end recordings of thousands of calls. Thisassessment methodology is applicable to both touch-tone IVR systems andspeech-enabled IVR systems. Because agent time is the major cost driverin call-center operations, the methodology advantageously quantifiescost effectiveness in the form of a single number that measures how muchagent handling time a telephone user-interface (IVR system) saves.

To quantify usability, task completion is refined into a set of IVRsystem automation rates, and the complete traffic through an IVR isrepresented as a tree, called a user-path diagram. Beyond evaluation,the methodology of the present invention has important implications fortelephone user-interface design. Assessment results provide concreteguidance on how to improve the interface. Furthermore, the benefit of aredesigned interface can be estimated, thus providing the businessjustification for telephone interface usability re-engineering.

As discussed above, in telephone user-interfaces, complete callsconstitute the only complete record of user and system behavior.Therefore, the methodology of the present invention for performing acomprehensive usability assessment of telephone interfaces is based onend-to-end recordings of calls. A call typically begins with a dialogwith an automated (IVR) system, which will be referred to as the IVRsection of the IVR system-caller dialog, which may be followed by adialog with a live agent, to be referred to as the agent-caller dialog.

The following sub-headings key into the various blocks shown in FIG. 1.

Collecting Data from Complete Calls (Recording) 2

Recordings of complete calls represent a large amount of data that isdifficult to analyze in its raw form. For example, as mentioned above,one hour of recording on one channel at 8 kHz with an 8-bit resolutioncorresponds roughly to 30 Mbytes of audio data. To make the analysis ofcall data efficient, various techniques are used to transform therecordings into a complete trace of significant events for each call.

Significant events of a call include, in the IVR section of a call,system prompts and caller input (touch-tone or speech), and, in theagent-caller dialog, exchange of various kinds of information, such asaccount numbers, dollar amounts, etc., and description of the caller'sproblem, e.g., a question about a bill, an inquiry into flightschedules. While most of the analyses are based on an event sequence,the ability to switch between a call recording and its representation asan event sequence is extremely advantageous throughout the analysisprocess.

The initial stages of the assessment method of the present inventioninvolve collecting data from complete calls and forming, from thecollected call data, a call event trace. The formed call event tracemust encompass both the IVR section and agent-caller dialog, if such adialog occurs during a call. The reports generated by conventional IVRplatforms are inadequate and inaccurate, because they do not track theevent sequence for a call. Instead, they merely report “peg” counts,which indicate how many times a prompt or menu was visited overall,without maintaining references to specific calls. Conventional IVRreports are inaccurate because they count any call that ended within theIVR section as “resolved”, regardless of whether the caller obtained anyuseful information.

The best method for capturing an IVR event sequence is an event log thatis generated by the IVR system itself, and is described in thedisclosure corresponding to reference numeral 3. However such IVRlogging requires the IVR program code to be modified to write to anevent log at appropriate states. This process is error-prone andintrusive to call-center operations. To avoid these disadvantages, theinventors have developed a method that infers the complete IVR eventsequence from a call recording, and which can be performed after thefact. Since calls can be recorded remotely, the event sequence can thusbe captured in an unobtrusive fashion.

The following section describes techniques employed to collect data fromcomplete calls, in particular the end-to-end recording technique 2 andthe IVR logging technique 3.

FIGS. 2A-2C

FIGS. 2A-2C illustrate three options for recording incoming calls to acall center.

In the first option, shown in FIG. 2A, recording is performed on-site atthe call center by placing recording equipment at the call center siteor by using a live observer to listen to the calls and manually note thesignificant events. The recording equipment or the live observer isconnected directly to the call center's incoming telephone lines and toa customer service representatives (CSR's) telephone handset.

Usually, the first option is used when a call center has a policy thatprohibits the off-site recording of calls or the recording of callsaltogether. Therefore, in order to be able to assess the performance ofthe call center, a live observer is used to listen to (observe) calls tothe call center and to manually make a record of events that occurduring the observed calls. During the IVR portion of the calls, theobserver uses a commercially available DTMF decoding device thatindicates which touch-tone buttons were pressed in response to variousinquiries made by the IVR system. If a call is transferred to a liveagent, the observer takes notes on (annotates) that portion of the callto manually create a record of the questions, answers, and/or commentsexchanged between the caller and the agent.

In practice, a recording operation according to the first optionproceeds as follows. A caller 1101 places a call to a call center 1102via a public switched telephone network (PSTN) 1103, a trunking system1104, and an automatic call director (ACD) system 1107. The ACD system1107 handles the queuing and switching of calls to the call center 1102.

If the call is to be recorded or observed in real time, a voice responseunit (VRU) 1105 of the call center's IVR system 1106 notifies the caller1101 that the call may be monitored for quality assurance or otherpurposes. Then, an electronic recorder or a live observer 1108 makes arecord of the DTMF signals from the caller's touch-tone responses toinquiries or prompts from the IVR system 1106. If the caller 1101 optsto speak with a live agent or customer service representative (CSR)1109, the recorder/live observer 1108 makes record of the caller-agentinteractions.

In the first option, recording/observation takes place via lines 1110between the recorder/live observer 1108, the trunking system 1104, andthe CSR 1109, as shown in FIG. 2A.

In the second option, shown in FIG. 2B, recording is performed off-siteat a dedicated recording facility or other suitable location. Thisoption requires the call center's ACD system 1107 to have one or moreobservation ports 1111 that enable external dial-in observation. Thatis, the call center's ACD system 1107 must have a software “wire-tap”feature that allows calls to be listened to. Some commercially availableACD systems, including ones made by Lucent™ and Aspect™, have such afeature for monitoring call quality.

In practice, an off-site recording operation according to the secondoption proceeds as follows. A VRU 1112 of a service observation system1113 running at an off-site recording facility 1114 places a call to thecall center's observation port 1111. The VRU 1112 provides theobservation port 1111 with a DTMF security passcode, which effectivelygives the VRU 1112 access to the call center's incoming calls. The VRU1112 then specifies an extension to be monitored and/or recorded. Itshould be understood that if the ACD system 1107 has several observationports then several extensions may be monitored and/or recorded.

Similar to the first option, when a caller 1101 places a call to a callcenter 1102, if the call is to be recorded, a VRU 1105 of the callcenter's IVR system 1106 notifies the caller 1101 that the call may bemonitored for quality assurance or other purposes. Then, a recorder 1115records the entire call, including the DTMF signals from the caller'stouch-tone responses to inquiries or prompts from the IVR system 1106,as well as the caller-agent interactions, if any.

In the third option, shown in FIG. 2C, recording is performed off-siteby using a router 1116 to route incoming calls to a VRU 1112 of anoff-site service observation system 1113. The calls are then redirectedor “tromboned” back to the router 1116 by a trombone unit of the VRU1112. Thus, calls are diverted by the router 1116 to the VRU 1112 of theservice observation system 1113, where some or all of the calls may berecorded. Optionally, the router 1116 may be programmed to decide whichcalls to divert to the service observation system 1113.

In practice, a recording operation according to the third optionproceeds as follows. A caller 1101 places a call to a call center 1102via a PSTN 1103. The call is then routed by a router 1116 to a serviceobservation system 1113 of an off-site recording facility 1114 via aredirection line 11171. The VRU 1112 of the service observation system1113 notifies the caller 1101 that the call may be monitored for qualityassurance or other purposes. The call is recorded by a recorder 1115connected to the service observation system 1113. A trombone unit 1118of the service observation system 1113 initiates a call back to therouter 1116 via a redirection line 11172, and the router 1116 thenroutes the call to the call center 1102 from which the call wasdiverted.

Then, similar to the first option, the call passes a trunking system1104 to the ACD system 1107, where it is sent to the IVR system 1106.The recorder 1115 records the entire call via the redirection lines11171, 11172, including the DTMF signals from the caller's touch-toneresponses to inquiries or prompts from the IVR system 1106, as well asthe caller-agent interactions, if any.

FIGS. 2B and 2C show the recorder 1115 as a separate unit from theservice observation system 1113. It should be understood, however, thatthe recorder 1115 need not be a discrete unit but instead can bephysically incorporated into the service observation system 1113.

For the options described above, it should be understood that all of theincoming calls need not be recorded and, instead, a selected percentageof all calls, a selected number of calls, or only calls occurring duringspecified times of the day may be recorded, for example.

Also, the options described above have various advantages anddisadvantages, and an option that may be ideal for one type of situationmay be totally unsuitable for another type of situation.

For example, the first option is the least flexible, because it requiresthe installation and maintenance of recording equipment at the callcenter or the use of a live observer set up at the call center to listento calls. Also, recordings must be transferred to another location foranalysis, unless analysis equipment (e.g., computers configured to runanalysis programs) is installed at the call center and trained personnelis available to run the programs at the call center. Further, if a liveobserver is used, he or she must be trained as to the call flow of thecall center's IVR system. That is, the observer must know the optionsavailable to the caller as the caller interacts with the IVR system.Also, the data collected by the observer must be put into a form thatcan be analyzed by a computer.

Despite the disadvantages of the first option, if a call center does notallow recording of calls or only allows recording to take place on itspremises, it may be the only way to obtain data necessary to make anassessment of the call center's performance, using the assessmenttechniques described infra.

The second option is the most flexible, because it allows calls to berecorded and analyzed at a facility dedicated to assessing call-centerperformance. It also enables selective analysis of specific aspects ofan IVR system. For example, a call center's IVR system may initiallyrequest a caller to indicate whether the call is related to a billinginquiry, an order inquiry, a product information inquiry, or otherinquiries. The second option enables the selective recording of, forexample, only those calls designated by the callers to be billinginquiries. This allows the call center to target one aspect of its IVRsystem at a time for assessment, by monitoring only particularextensions associated with that aspect, and prevents the unnecessaryrecording of calls related to aspects not being assessed.

The second option may be implemented as an automated process by settingup the service observation system to automatically place a call to theservice observation port of the ACD system at a designated time andprovide the ACD system with the appropriate access number. Recordingscan then be made for calls to the call center at selected hours of theday, at selected days of the week, for a selected number of calls, forexample. Because the recordings are made off site, that is, external tothe call center, there is minimal intrusion in the call center'soperations. Also, if the service observation system fails for whateverreason, this option will not affect incoming calls to the call center.

Optionally, a call center that handles a large number of calls may usemultiple lines or a high bandwidth line, such as a T1 line, to handlemultiple calls simultaneously. Of course, the second option is notavailable if the call center's ACD system does not accommodate externaldial-in observation. In such cases, the third option allows for calls tobe recorded by redirecting (tromboning) them through a serviceobservation system. An advantage of tromboning is that it allows for arandom sampling of calls from geographically distributed call centers tobe recorded in a central location. Tromboning may also be used to enablelive observers to listen to calls at a central location, if recording ofcalls is not feasible.

A disadvantage of tromboning is it precludes the use of features such ascaller-ID to identify telephone numbers of callers, because theidentified number will always be the telephone number corresponding tothe location of the service observation system, i.e., where the callsare being redirected. Another disadvantage of tromboning is that, shouldthere be a break in any of the legs of a tromboned call, e.g., a breakbetween the caller and the VRU of the service observation system or abreak between the VRU of the service observation system and the callcenters ACD system, the caller will be disconnected from the callcenter.

For the above options, calls can be recorded using, for example, astandard NT workstation (not shown). According to a preferredembodiment, the calls are recorded onto the workstation's hard disk, andthe recorded data is later transferred to a central server, which isconnected to a network, so that the recorded data may be accessed byvarious programs of the service observation system for analysis.Transfer of the recorded data frees the hard disk to record additionalcalls.

Typically, 1 minute of recording utilizes 1 M of hard disk space.Therefore, a workstation with 8 G of hard disk space can record 8000minutes of calls. Of course, the more calls that are recorded andanalyzed, the more accurate the assessment of the call center will be,based on statistics theory.

IVR Logging 3

When a caller interacts with a call center's IVR system, the IVR systemautomatically keeps a record of, or logs, all the significant eventsthat occur during the call. IVR logging is different from the end-to-endrecording of calls discussed earlier. IVR logging 3 is an automatedprocess performed by the IVR system itself to sequentially itemize thepath traversed by each call to the call center.

FIGS. 3A-3F

Essential to IVR logging 3 is a detailed flow chart for the callcenter's IVR system. An IVR flow chart maps out all the possible paths acaller can take while interacting with the IVR system. An example of anIVR flow chart is shown in FIG. 3A, which details all the optionsavailable when a call is made to a town's motor-vehicle department (DMV)call center. Another example of an IVR flow chart is shown in FIG. 3B,which is a combination of FIGS. 3C-1 and 3C-2. FIG. 3B details all theoptions available when a call is made to a telephone company's callcenter. As shown in each of the IVR flow charts, all the inquiries orprompts given by the IVR system are itemized, as well as all thepossible touch-tone responses a caller can make to each prompt.

The flow of a call through the IVR system consists of a series of“states” connected by “transitions.” A state corresponds to atransaction between the IVR system and another entity, such as thecaller, a database, or the ACD system, for example. The outcome of atransaction usually is a transition, which is a progression from onestate to another state. Of course, if the caller requests the IVR systemto repeat a list of choices, the transaction goes back to the precedingstate without a transition occurring.

An IVR system 1201 is schematically shown in FIG. 3D. A VRU 1202 of theIVR system 1201 is a computer that performs the functions of a customerservice representative according to a program stored in a memory 1203 ofthe IVR system 1201. That is, the VRU 1202 serves as an interfacebetween the caller and the IVR system 1201. The program implements thecall center's IVR call flow and runs interactively in response totouch-tone buttons pressed by the caller.

The IVR system 1201 also includes a DTMF decoder 1204, which translatestouch-tone sounds into their corresponding numbers. A memory 1205 of theIVR system 1201 sequentially stores in a file, for each call, an eventlog of the inquiries and prompts given to the caller, as well as thecaller's touch-tone responses. Each file contains the complete eventsequence for the IVR portion of the call, and may be stored as part ofan event-sequence database of the memory 1205. The memory 1205 alsostores data indicating at what point in the call flow the caller leavesthe IVR system 1201, and whether the caller terminated the call (hungup) or was transferred to a live agent.

Optionally, a speech recognition system (not shown) may be included inthe IVR system 1201 to enable the program to run interactively inresponse to the caller's spoken words.

To prepare an IVR log of calls made to the call center, the data storedin the memory 1205 is downloaded to another computer, where it isanalyzed to determine the caller's responses to inquiries or promptsfrom the VRU 1202. That is, the other computer translates the recordeddata for each call into information about the path through the IVR callflow traversed by the call, and translates the DTMF responses made bythe caller at each state of the call.

Optionally, instead of downloading the data stored in the memory 1205 toanother computer for analysis, the analysis can be performed by aprocessor (not shown) internal to the IVR system 1201.

In addition to determining the states and caller responses for eachcall, the analysis also determines the time of day of the call and howlong it takes for the caller to respond to an inquiry or prompt, thatis, how long it takes for the caller to make the transition from onestate of the IVR call flow to another. Thus, IVR log entries for a callenable the call to be reconstructed in sequence, including the time ofthe call and the timing of transitions from state to state.

FIG. 3E shows a chart of typical data collected for a call. For eachstate traversed by the call, the outcome is listed. Also, the time ofentry to each state is listed, which can be used to determine how longit takes the caller to make the transition to each state.

FIG. 3F shows an IVR log that includes an area for identifying thecaller. This enables more than one call to be represented in a singleIVR log, without causing confusion as to which call a particular entrycorresponds to.

The IVR logs shown in FIGS. 3E and 3F keep track of what information, ifany, is delivered to each caller. Therefore, if a caller repeatedlyrequests a menu to be replayed because the options are too numerous andconfusing, and then eventually hangs up before any information isdelivered, then the IVR log entries for that call will indicate how manytimes the caller needed the menu to be repeated before hanging up. Theentries will also show that the caller did not receive usefulinformation. If the IVR log has many such entries, then re-engineeringto improve the call flow of the IVR system may be justified.

In contrast, conventional IVR logging techniques that merely tally thenumber of times a state is traversed in an IVR system provide no insightas to why a particular state is traversed more than another state. Suchconventional systems would not reveal, for example, that the reason amenu state was traversed a disproportionately large number of times wasbecause callers needed to listen to that menu more than once beforeunderstanding the options. Therefore, a detailed log of the states andtransitions of the IVR portion of calls enables a more accuraterecreation of what occurred during calls to a call center, thus enablinga more accurate assessment of the call center's IVR system byfacilitating the identification of weak aspects of the IVR system. Asdiscussed in more detail below, an improved IVR system, in which callersobtain the information they are seeking in the least amount of time andwithout the use of a live agent, reduces the costs involved in runningthe call center.

IVR logs may be summarized to produce a transition report, which givesstatistical details on, for example, the number of calls that made thetransition from one particular state to another particular state; thenumber of calls that were abandoned at a particular state; the number ofrequests for a live agent at a particular state; and the number of timesno transitions were made from a particular state because callersrequested a repeat of the menu options. As will be discussed below, thestatistics provided by a transition report can be used to produce auser-path diagram, which reveals the percentage of callers making thevarious transitions in a call center's IVR call flow.

If a caller requests to speak with a live agent, the log file for thatcall will include data indicating the transfer time and destination, sothat the log file can be matched with a recording of the caller-agentinteraction to enable a complete recreation of the entire call. Therecorded caller-agent interaction contains the “ground truth” of thecall, and provides information on whether the caller's question couldhave been answered during the IVR portion of the call if the IVR callflow was re-engineered.

IVR logs also may be used to generate an error report, summarizingerrors made by callers, including errors in entering account numbers andtime-out errors, which occur when a call is assumed to be abandonedbecause no response was entered within a pre-set time period. An errorreport is used to determine trouble areas in a call center's IVR callflow, so that those areas may be re-engineered to improve the callcenter's effectiveness.

Further, IVR logs may be used to summarize timing statistics of events,which are useful in understanding whether to develop a shorter route tocertain commonly requested information than what a current IVR call flowallows.

IVR System Analysis 6

IVR system analysis 6, introduced in FIG. 1, is a computer-based methodfor determining the complete sequence of events in an IVR system-callerdialog, based on a recording of at least the IVR section of a call.Recordings of calls can be collected in a completely unobtrusivefashion, and IVR system analysis determines the complete IVR eventsequence independently of data from IVR logs.

IVR system analysis determines the complete IVR event sequence in twosteps: event detection and prompt inference. The event detection stepidentifies user input, which may be touch-tone or speech input, andselected system prompts. The prompt inference step requires: 1) completeknowledge of the user input; 2) partial knowledge of system prompts; and3) knowledge of the call-flow logic of the IVR system. Using thisinformation, the prompt inference technique of the present inventioninfers the complete IVR event sequence.

Complete IVR event sequences are necessary for detailed IVR assessments,including the generation of user-path diagrams, automation analyses, androuting analyses techniques, which are discussed in more detail below.IVR system analysis is applicable to touch-tone, speech-enabled, andmultimodal IVR systems.

IVR system analysis 6 employs three main tools to capture the eventsequence for the IVR portion of a call: a prompt detector, a DTMFdetector, and a prompt inference tool. The prompt detector and the DTMFdetector are used for event detection. Optionally, if evaluating aspeech-enabled IVR system, a speech detector preferably would beemployed to identify spoken user input in the recordings. Any knownspeech detection algorithm may be used for this purpose. A commerciallyavailable DTMF detector preferably is used to detect touch tones. Aprompt detector, to be described in detail below, preferably is used toidentify spoken prompts in the recordings. The prompt detector employsstandard pattern recognition techniques, in particular, templatematching, and identifies prompts by matching audio samples of promptswith the audio signal of the recordings.

Finally, for all but the simplest IVR systems, a prompt inference toolis used to infer a complete prompt sequence. While it is possible todetect all prompts in a call using a prompt detector, this isimpractical for complex IVR systems, because the detector would needsamples for each prompt, and complex IVR systems may contain hundreds ofdifferent prompts. The prompt inference technique of the presentinvention determines a complete prompt sequence from the completesequence of user input and knowledge of the call-flow logic. Accordingto the prompt inference technique of the present invention, it isnecessary to detect only a subset of prompts necessary to disambiguatethe complete sequence.

FIG. 4 illustrates a complete IVR system analysis process, whichconsists of four steps. Optional steps are illustrated with dottedcircles. In FIG. 4, first a recording step 101 is performed. Raw audiorecordings are subjected to wave form conversion in step 102. Theresulting signal, which may contain one or more calls, is split by acall splitter in step 103, such that each call is contained in exactlyone audio file. This step is not required if the recording equipmentdelivers recordings that contain only one call per file. Second, in step107, a complete sequence of user input is detected using a DTMF detectorfor touch-tone input. Optionally, in step 106, a speech detector is usedto detect spoken input. In addition, selected prompts are detected usinga prompt detector in step 105. Prompt samples 109 are used to configurethe prompt detector. As an optional third step, a call classifier may beemployed to identify specific kinds of calls in step 108. Callclassification is necessary if the recording equipment captures callsfrom multiple sources and only one specific source is of interest. Callscan be classified by detecting prompts that uniquely identify each kindof call that is of interest. For example, the greeting prompt oftendiffers for various kinds of calls.

Prompt inference, step 110, is the final step in IVR system analysis 6.In addition to the complete sequence of touch-tones, prompt (sequence)inference requires complete knowledge of the call-flow logic (orspecification) 111 and at least a partial sequence of detected prompts.The prompt inference technique models the call-flow logic as anon-deterministic finite-state automaton; the states correspond toprompts, and state transitions are triggered by touch-tone sounds,speech, or the absence of user input. Each transition represents aspecific user response, for example, pressing 1 may lead a caller to oneprompt while pressing 2 or providing no input would lead the caller to adifferent menu. Prompt inference determines a complete IVR eventsequence by determining which state sequence(s) are consistent with thedetected user input and the call-flow logic 111. Algorithms thatdetermine a state sequence in a finite-state automaton for a specificsequence of input are in the public domain.

While standard algorithms can determine a complete event sequence intheory, ambiguity in interpreting a sequence of user input may arise forseveral reasons:

-   -   (1) Because callers frequently type ahead, i.e., press a        touch-tone before hearing the corresponding prompt, there is        ambiguity in tokenizing a complete sequence of DTMF tones into        discrete responses to specific touch-tone prompts.    -   (2) Event detection is not completely accurate. Therefore, there        may be errors in the detected touch-tones and prompts.    -   (3) The call flow sometimes depends on internal processing,        which cannot be inferred from the recording. For example,        callers with an overdue account may be handled differently from        other callers.    -   (4) Exactly how the caller left the IVR portion of the call, the        so-called IVR exit condition, tends to be another source of        ambiguity.

Prompt inference resolves the ambiguity in three steps. First, a set ofprompts is determined that allows all ambiguities to be resolved, andthese prompts are detected in step 105. All prompts that immediatelyfollow any ambiguous transition in the call flow must be detected.Ambiguous transitions occur whenever the next state depends on internalprocessing, such as accessing a database, and whenever a timeout (i.e.,no user input) leads to a specific new path. Second, the algorithmmentioned above infers all state sequences (i.e., interpretations) ofthe detected user input that are consistent with the call-flow logic.Third, an interpretation is selected that is most consistent with thedetected prompt sequence.

Inferring exactly how the caller left the IVR portion (the exitcondition) requires special treatment. The exit condition indicateswhether the call ended in the IVR portion with a hangup or whether itwas transferred to an agent. Because all IVR systems announce transfersto agents to the caller, for example with the prompt “Please wait forthe next customer service representative,” whether a call wastransferred to an agent can be determined by detecting a transferprompt. If the prompt detector detects a transfer prompt, it is inferredthat the call was transferred to an agent; otherwise, it is assumed thatthe caller hung up. This method of inferring the IVR exit conditionfails when the caller hangs up during the hold time, before reaching anagent. However, for the purposes of IVR system analysis, thisdistinction is not made, and has not been found to be necessary. Rather,it is simply indicated whether or not a call was transferred to anagent. If necessary, annotation analysis 7, described below, candetermine whether a call that was transferred to an agent actuallyreached the agent.

The event sequence in speech-enabled IVRs can be captured in a similarway, with the following modification to the process. Unlike intouch-tone IVR systems where user input is limited to touch tones, whichcan be reliably recognized, recognition of user speech input iserror-prone. Therefore, a state transition after speech input cannot bereliably inferred, unless the state transition only depends on whetherthe user spoke at all. The simple presence of spoken input can bedetected reliably by a speech detector, shown as optional step 106 inFIG. 4. However, in all other cases, all prompts following spoken userinput must be detected to disambiguate the complete event sequence.

That is, for a speech-enabled IVR system, the analysis must rely onprompt detection to disambiguate an event after any speech input from acaller. And, to evaluate speech recognition performance, all segments ofrecording that contain user speech must be identified, and the truesequence of words on those spoken segments must be annotated manually,using human transcribers.

The inferred sequence of IVR events that is produced by the promptinference technique may be utilized by the assessment methodology of thepresent invention, for example, to assist the annotation efforts oftranscribers, in step 112, in generating a full transcript of a call.The sequence also is used for statistics generation in step 113, whichuses it, along with the call-flow specification (logic) 111, to generatea user-path diagram 9 and as input for automation analysis 8, to bedescribed in more detail below.

Transcription analysis captures the sequence of significant events foranything that follows the IVR portion of a call, i.e., waiting on holdand agent-caller dialogs. Significant events include: start of anagent-caller dialog, exchange of information between caller and agent(such as account numbers, amounts, dates), and reason for calling. Inaddition, transcription analysis may characterize a call as a wholeaccording to certain attributes, such as the level of call resolution(was the call resolved or not?) and agent courtesy. The presentinvention, in accordance with a preferred embodiment, employs humantranscribers to perform transcription analysis.

Annotation 7

The annotation techniques of the present invention provide a system to,in one embodiment, automatically index customer (caller) contacts tostore them in a semantically annotated database, which may be queried.Customer contacts in this context include, but are not limited to, callsplaced into a call center. They may instead be email received or Webpages accessed. The techniques also may be performed manually.

Annotation of customer contacts may include characteristics of acontact's content and quality. Characteristics of a contact may includelength of significant sections of the contact (e.g., in a call, timespent in the IVR and on hold and talking to a live agent) and mode oftermination of the contact (e.g., in a call, whether the caller hung upin the IVR portion, while on hold, or after talking to a live agent).Content refers to the reason(s) for the contact, the topics/issues“discussed” in the dialog(s), the information exchanged, thetransactions performed, and entities mentioned (e.g., companies).Quality refers to the success of the contact, customer satisfaction, thequality of the communication with the customer/caller.

Certain kinds of annotations are required input for several analysistechniques in an IVR system assessment. For example, in the routinganalysis 1 and IVR performance monitoring 3 techniques, each to bedescribed in detail below, the true call type, i.e., the “ground truth”or reason for a call, must be annotated; automation analysis 8 requiresannotations of automatable transactions or, at the very least, the calltype distribution; a life-of-call diagram 10 requires annotation of thelengths of various sections of a call.

Once indexed as a content database of customer contacts, standardinformation retrieval methods can be applied to mine this database ofcustomer contacts. An application for such a retrieval system includes,but is not limited to, customer relationship management (CRM).

A database of customer contacts, e.g., calls, can be annotated eithermanually or automatically. Computer-based annotation methods will bedescribed in detail next. Manual annotation techniques analogous to thecomputer-based methods can easily be inferred based on the followingdescription.

FIG. 5

An architecture for a computer-based indexing system is illustrated inFIG. 5. IVR system analysis and prompt inference techniques 201,described above, are used to determine a complete event sequence for theIVR portion of a call. In a manually effected embodiment, a humantranscriber listens to recorded calls to distinguish various features tobe extracted from the caller-agent dialog portion of a call. In anautomated embodiment of annotation 7, in accordance with the presentinvention, standard audio mining methods are configured to extractvarious features from the caller-agent dialog portion of a call asfollows.

Audio mining techniques using, for example, a speech/non-speech detector203, a speaker change detector 204, a large-vocabularyconversational-speech recognizer 205, a topic detector 206, and anamed-entity detector 207 are known. The speech/non-speech detector 203identifies speech and certain non-speech events, e.g., silence, muzak,hangup noise, in the audio signal. The speaker change detector 204 marksspeaker turns; the speech recognizer 205 outputs sequences of words forevery speaker turn; the topic detector 206 determines a topic (or othercontent characteristics) at various levels, and the named-entitydetector 207 identifies named entities, e.g., companies, people,locations, dollar amounts, dates. Data generated by these units serve asinput to an annotation algorithms unit 208, which annotates calls.Specific annotation algorithms for call characteristics, content, andquality are described in the following section. Semantic annotationsthat are generated by the annotation algorithms unit 208 are input to acustomer contact content database 210.

Call characteristics, such as length and termination mode, can bedetermined as follows. The IVR portion of a call, including a completesequence of IVR events, such as prompts and user responses, is annotatedbased on output from the IVR system analysis unit 201. The IVR systemanalysis unit 201 takes into account the call flow model 202 for thecall (contact) center being investigated when analyzing an audio signal.The output also includes the IVR exit condition, i.e., whether thecaller hung up in the IVR portion or whether the caller was transferredto a live agent. If the caller hung up, the exact time of the hangup isinferred from output from the speech/non-speech detector 203, either bydetecting hangup noise directly, or by inferring it from the presence ofa long silence at the end of the recording of the call. If the callerwas transferred to a live agent, a hold section is determined as thesection between a transfer prompt, the IVR prompt that announces atransfer to an agent, e.g. “Please wait for the next available agent,”and the first occurrence of a caller-agent dialog, which can be inferredfrom output from the speaker-change detector 204.

The agent-caller portion of a call is first annotated with speakerturns, using output from the speaker-change detector 204. Which speakeris the agent versus the caller can be determined using a speakeridentifier (not shown). Algorithms for speaker identification are alsoin the public domain. Additional information, however, is necessary toidentify agent versus caller. A database with speaker models from allagents in the call center can be used, or the agent can be inferred asthe first speaker after a hold section.

The content of a call is annotated using the topic detector 206 and thenamed-entity detector 207. The topic detector 206 is trained to annotatevarious kinds of topics using a set of calls that were manuallyannotated with topics. Once configured in this way, the topic detector206 can be used as is.

The whole call is annotated with all topics that were discussed in thecourse of the agent-caller dialog using the topic detector 206. If onlyone topic was discussed, this yields immediately the reason for the call(or “call type”). If more than one topic was discussed, the reason forthe call is determined to be the first topic or is inferred from the setof discussed topics using predetermined rules. These rules must bedetermined by an expert in the domain of the call center. Other content,in particular exchanged information and completed transactions, can beannotated using a topic detector configured to detect content other thantopics. For example, the topic detector 206 can detect 1 informationexchanges and transactions if it is trained on a set of several hundredcalls for which all information exchanges and transactions have beenmanually annotated. In certain cases, output from the named-entitydetector 207 can be used as additional input to the topic detector 206.For example, to detect stock transactions, all references to specifickinds of stock can be detected as the name of the respective company inthe vicinity of the word “shares.”

Automatic annotation of quality aspects can be performed similarly. Tothis end, specific quality aspects of interest preferably are annotatedmanually in several hundred calls. For example, sections are markedwhere an agent interacted with a customer in a polite manner and in arude manner, and sections are marked where a caller was satisfied andwhen a caller was frustrated. The same statistical models that are usedfor topic detection can then be trained to detect specific qualityaspects, e.g., sections of a call where the agent was rude or in whichthe caller was angry.

Mini Assessment 5

The above data collection techniques compile detailed call sequences onthe basis of end-to-end recordings of calls. A technique that isreferred to as mini assessment is used to create a rough on-lineannotation of complete calls using manual monitoring of complete calls,including both the IVR and caller-agent dialog portions of calls.

Mini-assessment is a process for assessing the effectiveness of a callcenter's IVR system by analyzing the path a caller takes whileinteracting with the IVR system. The true reason, or “ground truth,” ofthe call, is then determined from a conversation between the caller anda live agent. By comparing an implied reason for the call, based uponthe caller's menu selections while interacting with the IVR system, withthe ground truth of the call, based upon comments and questions thatarise in the caller/agent conversation, the effectiveness of the callcenter's IVR system is revealed.

For example, if the caller, during the IVR portion of the call, cyclesthrough the same menu repeatedly before requesting to speak with a liveagent, then asks the agent for information that is available through theIVR system, there is a clear indication that the menu options are toohard to understand, are too numerous to remember, and/or do notaccurately reflect the information available through the IVR system.Thus, by knowing the ground truth of the call, as gleaned from thecaller's interaction with the agent, a weakness or inefficiency of theIVR system is identified. Similarly, if callers, during the IVR portionof calls, repeatedly cycle through a particular menu and then hang up,there is an indication that the menu needs improvement.

Re-engineering to improve the call flow of the IVR system, in order tomore efficiently handle calls entirely within the IVR system and toreduce the time spent by agents on calls, results in reducing the costsassociated with maintaining the call center, especially if calls can behandled entirely by the IVR system without agent interaction. In orderto accurately pinpoint areas requiring re-engineering, a statisticallysignificant number of calls must be analyzed. As is well understood inthe field of statistics, the more calls that are analyzed the moreaccurate the assessment of the call center can be. Also, depending onthe complexity of the call center's IVR system, that is, the number ofpaths a caller may take while interacting with the IVR system, astatistically meaningful number of calls may vary from tens of calls toover a thousand calls.

In the mini-assessment process, an observer or analyst listens to callsto the call center from beginning to end. The analyst may listen to livecalls in real time or may listen to end-to-end recordings of calls. Thetechniques described earlier may be used to observe and/or record thecalls.

FIGS. 6A-6C

A flow chart summarizing the mini-assessment process is shown in FIG.6A. In step S1, information is gathered regarding the call center,including the call flow of the call center's IVR system and thedifferent categories of live agents that callers may be transferred to.The information also includes a complete set of destinations that theagents can transfer callers to, as well as rules regarding when suchtransfers are made. The analyst studies the information to understandthe different possible routes callers may take when interacting with theIVR system.

In step S2, a coding sheet is developed for the call center. An exampleof a coding sheet is shown in FIG. 6B, which is a combination of FIGS.6B-1 through 6B-4. The coding sheet summarizes the call flow of the callcenters IVR system into a list of items that the analyst can quicklycheck off when analyzing a call, and also summarizes the interactionbetween the caller and a live agent, if any. Also, because the analystlistens to the call even during the IVR portion, when the callerinteracts with the IVR system the analyst can make note of any verbalcomments made by the caller that would be helpful to identify thepurpose of the call.

The items listed on the coding sheet include menu choices made by thecaller when interacting with the IVR system; whether and how the callerwas transferred to an agent; functions performed by the agent; whetherthose functions could have been handled by the IVR system; whether theagent transferred the call to another agent and, if so, the destinationof the transfer; whether the first agent stayed on the line during thetransfer; whether the call was further transferred to another agent,etc.; and when the caller terminated the call (hangs up).

The coding sheet is also used to note timing information, including timespent interacting with the IVR system; time spent on hold; time spentinteracting with the live agent; time spent on transfers; and the totaltime of the call.

In step S3 of the flow chart of FIG. 6A, the analyst listens to andtakes notes on live calls, from the time when the caller initiallyinteracts with the IVR system through the time when the caller hangs up.This is repeated for a desired number of calls. Then, in step S4, theanalyst's notes are entered on the coding sheet for the call center.Optionally, if the call center allows recording of calls (step S7), theanalyst listens to recorded calls and “codes” (fills out the codingsheet) while replaying the calls (step S8).

As shown in FIG. 6B, the coding sheet is a spreadsheet in which items tobe coded are listed and each call is summarized in a column of thespreadsheet. Space is provided for summarizing various aspects of thecall. Items marked with “1” indicate that they apply for that call.Items left blank or marked with “0” indicate that they do not apply forthat call.

The coding sheet of FIG. 6B includes entries for the date and time ofthe call, the caller's telephone number and gender, and identificationletters for the observer (analyst). The upper portion of the codingsheet also provides information on whether the call was fully automated,whether an agent was involved, and the duration of the call.

In the IVR Summary section of the coding sheet, the observer lists themenu choices made by the caller. The menu choices are determined using aDTMF decoder, which decodes the touch-tone selections made by thecaller.

If the call was incomplete, that is, if the caller abandoned the callduring the IVR portion without receiving any requested information, thecoding sheet notes the point at which the call was abandoned. If thecall was transferred to a live agent, the coding sheet notes how thetransfer took place. The coding sheet notes the menu choices made in theIVR portion of the call, as well as the functions completed there, ifany.

For calls that were transferred to a live agent, space is provided onthe coding sheet for the analyst to insert comments on the problem posedby the caller and how it was solved by the agent. The coding sheet alsonotes whether the agent performed functions that could have beenperformed by the IVR system, as well as whether the agent performedfunctions that can potentially be handled by the IVR system, if sore-designed. Functions that cannot be handled by the IVR system are alsonoted on the coding sheet.

Once the desired number of calls has been coded, the results aretabulated and studied to look for patterns or trends indicating areas inwhich there is inefficient or unnecessary use of agent time (step S5 ofFIG. 6A). That is, if a certain function is currently being performed byan agent, but which could easily be performed by the IVR system, thenthat function is identified as an area that can be improved. Also, asmentioned earlier, if callers cycle through a particular menu more thanonce, then that menu is identified as an area that can be improved.

FIG. 6C, which is a combination of FIGS. 6C-1 through 6C-4, shows asummary of tabulated results from an analysis of calls to a call center.Statistics are given for an average duration of IVR portions of calls,an average duration of entire calls, the number and percentage of callsthat were fully automated, the number and percentage of calls thatinvolved a live agent, etc. The summary of tabulated results utilizesthat same categories as the coding sheet, so that statistics fromvarious aspects of the coding sheet may be easily found.

Finally, in step S6 of the flow chart of FIG. 6A, an analysis report isproduced based on the tabulated results from step S5. An example of ananalysis report is shown in FIG. 6D. In the “Data” column on the left,problems are listed; in the “Conclusion” column on the right,corresponding diagnoses are listed, as well as suggested improvements.

The report describes three general categories to be improved: QuickHits; Touch-Tone Re-Engineering; and Speech Engineering. In the QuickHits category, simple improvements that can be easily implementedwithout major cost to the call center are suggested. Such simpleimprovements include rewording, re-phrasing or re-ordering prompts inthe IVR system, menu selections, and other recorded messages that arereplayed to callers; adding or removing prompts and menus in the IVRsystem; and enforcing certain call center policies. For example, agentsmay be inefficiently staying on the line with callers until transfersare completed (“warm transfer”), even though the policy of the callcenter is to transfer calls without remaining on the line. Also, agentsmay not be using special transfer queues while they remain on the lineduring a transfer, thus further increasing the time spent on calls.

In the Touch-Tone Re-Engineering category, improvements to the call flowof the IVR system are suggested that are more difficult to implementand/or incur major costs. Such improvements include areas that currentlyare being handled by live agents, but could also be handled by the IVRsystem if it was re-engineered. For example, the IVR system may bere-engineered to request a caller to enter, via touch-tone keys, a birthdate, a portion of a social security number, a car license plate number,etc., which the IVR system uses to verify/locate the caller's recordsprior to transferring the call to a live agent. This would save timenormally spent by the agent to enter the information into a terminal andwait for the caller's records to appear.

Similarly, if it turns out that callers frequently enter wrong accountnumbers or wrong birth dates, etc., then the IVR system may bere-engineered to request re-entry of a previously entered number, sothat a comparison can be made and the caller notified if the entries donot match. Also, the IVR system may be re-engineered to request morethan one type of identification information, usually requested by a liveagent, so that a cross reference can be made by the IVR system to verifythat the caller is authorized to access certain information from the IVRsystem before transferring the call to an agent.

In the Speech Engineering category, improvements in or implementation ofspeech recognition technology is suggested for the IVR system. Forexample, if the call center receives a large percentage of its callsfrom elderly callers and callers with certain physical handicaps orcallers calling from cellular phones, it may be easier for those callersto speak their responses to prompts from the IVR system than to entertheir responses via touch-tone keys. Those categories of callers arelikely to pretend that the call is from a rotary telephone, to avoidinteracting with the IVR system. The call center would reduce agent timeby re-designing the IVR system to provide a speech recognition option.

The analysis reports generated in step S6 are then used by the callcenters to identify and improve areas of inefficiency in their IVRsystems. As discussed infra, significant cost savings can be achieved byreducing the amount of time spent by live agents handling calls. This isdone by identifying functions that can be completed through the IVRsystem and providing menu selections notifying callers of thosefunctions. Agent time can also be reduced by identifying and eliminatingareas of caller confusion in the IVR system, which usually result incallers opting out of the IVR system by directly requesting to speakwith an agent or by callers pretending to be calling from a rotarytelephone.

Automation Analysis 8

The main cost driver in the operation of call centers is the cost ofagents. The ratio between cost of agents and all other costs, includingtelecom time, and IVR system hardware and maintenance, is at least 4:1.Therefore, the assessment methodology of the present inventionquantifies the cost effectiveness of an IVR system in terms of agenttime.

The total IVR system benefit (cost savings) is defined herein as agenttime that is saved by the IVR system, compared to routing all callsdirectly to an agent. An IVR system “saves” agent time whenever itsuccessfully performs tasks that otherwise would be performed by anagent. Tasks that typically can be performed within an IVR systeminclude identifying the caller, providing information to the caller,performing transactions, and routing the caller to an appropriate agent.In some cases, completing these tasks successfully may resolve the calland the caller hangs up without any assistance from an agent. Such casesof complete call resolution will be referred to as full automation.

In other cases, the reason for the call is not completely resolved bythe IVR system, and the caller is transferred to a live agent. Althoughnot fully automated, completing one or more tasks in the IVR systemreduces the time that the agent has to spend with the caller. Such caseswill be referred to as partial automation of calls. In summary, an IVRsystem saves agent time by achieving different levels of automation of acall.

In the two subsections that follow, IVR system automation is formalizedand a method of quantifying cost effectiveness of an IVR system in asingle measure, the total IVR system benefit, as measured in agentseconds, is described. While the emphasis in this context is on cost, itis noted that IVR system automation rates correspond to task completionrates. Hence, IVR system automation is a more differentiated or specificversion of this standard usability measure. Thus, total IVR systembenefit can be interpreted to quantify both cost benefit and usabilityof an IVR system.

IVR system automation can be measured based on call state sequence data.This process is referred to as (IVR system) automation analysis. Anautomation analysis proceeds in the following three steps: definingautomation categories, collecting call profile statistics, andcalculating automation rates.

In defining automation categories, all tasks that can be completedwithin an IVR system are classified into a few distinct categories, suchas caller identification, information delivery, transaction completion,and routing. The completion of these tasks is typically associated withreaching a certain state in the IVR system. Therefore, a set ofcompleted tasks can be inferred directly from the event sequence datafor a call, using a simple lookup table that documents which IVR statesindicate the completion of certain tasks.

With such a categorization of tasks that can be completed within an IVRsystem, different levels of automation correspond to a distinctcombination of automation categories, which is referred to as a callprofile. Given a set of calls and their event sequence data, automationanalysis 8 accumulates counts for each call profile by annotating everycall with its set of completed tasks. The call traffic into an IVRsystem is thus partitioned into a set of call profiles, eachrepresenting a distinct level of automation.

Automation rates, finally, are defined as the percentage of automationachieved in each automation category. This percentage can be calculatedby adding the percentages of all call profiles that contain a certainautomation category. Task completion in an IVR system is thus quantifiedas automation rates.

As mentioned above, the IVR system automation analysis 8 of the presentinvention advantageously quantifies the level of automation achieved byan existing IVR system, as well as the potential for improvement.Furthermore, automation analysis 8 quantifies the total (automation)benefit of an IVR system in a single measure, which can easily betranslated into cost savings estimates.

Automation is measured by categorizing the manifold transactions that anIVR system may offer into four broad automation categories, annotatingIVR event sequences with the level of automation achieved, andannotating all automatable transactions that occur in agent-callerdialogs. Based on such annotations, existing automation rates and upperbounds can be estimated for each automation category, as describedabove.

In addition, by obtaining reasonable estimates for how much agent timeis saved by each automation category, the automation rates can betransformed into a single measure for the total IVR system (automation)benefit in terms of agent seconds saved by the IVR system, simply bymultiplying automation rates with the time saved and summing up acrossautomation categories.

IVR system automation analysis 8 includes two parts: first, measuringautomation as achieved in an existing IVR system, and second, measuringthe potential for increasing automation.

FIG. 7

FIG. 7 illustrates the process for measuring existing automation levelsof an IVR system. As discussed below, the process illustrated in thefigure provides data for a spreadsheet that graphically conveys theautomation benefit.

As shown in FIG. 7, step S10 utilizes stored IVR event sequences 301 anda lookup table 302 as input. The lookup table has, for each applicableIVR event, the automation achieved by that event. Step S10 firstannotates sequences with the automation achieved, to indicate suchautomation as capture of a telephone number or readout of information(e.g., a customer's balance). The data for every call, that is, the IVRevent sequence, is collapsed into counts for distinct call profiles.Finally, the counts for each call profile are converted into theirrelative percentages. The output of step S10 consists of call profileswith counts, e.g., self-serve 5.6%, to specialist with readout 1.8%.

At step S20, a spreadsheet is formed with rows showing the call profilesp_(i), and columns showing the relative frequency f_(i) of the callprofiles p_(i), the total benefit b_(i) of the call profiles p_(i),which is the sum of the benefits of all relevant automation categories,and the savings potential s_(i) for the call profiles p_(i). Thespreadsheet yields automation rates and the total IVR system benefit.

The following formulas specify the main calculations in the spreadsheet:

-   -   1. Benefit for profile        ${{p_{i}:b_{i}} = {\sum\limits_{j}{{A\left( {i,j} \right)}B_{j}}}},$        where A(i, j)=1 if automation category j is achieved/completed        in profile p_(i) and 0 otherwise, and where B_(j) is the benefit        assumption for automation category j.    -   2. Total IVR benefit $B = {\sum\limits_{i}{f_{i}{b_{i}.}}}$

FIGS. 8 and 9

FIG. 8 shows an example of a spreadsheet generated as described above toassist in IVR system automation analysis 8 in accord. The left columnlists the call profiles. This analysis distinguishes two agent types,“specialist” and “floor.” The next two columns (labeled “Traffic”) showthe breakdown of the total data set, which consists of 3636 calls, intovarious profiles. For example, 2% of the calls were fully automated, and18.7% abandoned without providing useful information. Then, the three“Automation” columns show the automation categories for each profile.The analysis is based on three automation categories: “A” for capture ofthe caller's account number, “R” for routing, and “I” for delivery ofinformation. For example, the profile “Transfer to floor after inforeadout” achieved capture of the account number (“A”) and automateddelivery of information (“I”). The bottom row, for the three“Automation” columns, show the automation rates by category: 41.5%capture of account number, 14.5% routing, and 3.1% information delivery.The last two columns and the rows labeled “Misrouted . . . ” aredescribed below.

Looking at cost effectiveness from an operational point of view, thetotal IVR system benefit was defined earlier as saved agent time. TotalIVR system benefit can be directly quantified by measuring the averagetime that a caller spends with a live agent, and comparing that time fora call center that does not use an IVR system, as a baseline. Thebaseline (no IVR system) can be simulated by routing all callersdirectly to an agent. Evaluating this baseline with live customertraffic, however, is impractical, because bypassing a deployed IVRsystem would result in a severe disturbance of the on-going call centeroperation. A call center must provide the best possible customer serviceat all times. Therefore, instead of measuring total IVR system benefitdirectly, the present invention advantageously provides a method forestimating the total IVR system benefit based on the results of theautomation analysis.

Automation analysis 8 represents completion rates for all tasks that canbe automated with an IVR system. The missing piece to estimate IVRsystem benefit is finding out how much agent time is saved by completingeach task or automation category. The savings in agent time for eachautomation category can be measured based on a transcription analysis ofagent-caller dialogs. Transcription analysis inserts codes forcompleting tasks within agent-caller dialogs. Based on these codes thepresent invention allows to be estimated, for each task category, howmuch time agents spend on the task. This corresponds exactly to the timesaved by completing the same tasks in the IVR system.

Alternatively, if such timing data is not available, all the timings maybe replaced by standard benefit assumptions, as shown in FIG. 9. Theseassumptions represent lower bounds and are based on experience gatheredfrom many call centers.

Given the time saved of agent-caller dialog in each automation category,the average agent-time saved by the IVR system can be determined easilyby multiplying the corresponding automation rate with the time saved. Inthe example shown in FIG. 8, the two columns labeled “Benefit” show theIVR system benefit for each call profile. The left column shows thebenefit for one call and the right column the average benefit, relativeto all 3636 calls that were evaluated.

For example, the profile “Transfers to floor after info readout”indicates that the caller provided the account number to the IVR system(A, saving 15 agent seconds) and listened to useful information in theIVR system (I, saving 40 agent seconds). Adding these up, this profilecorresponds to a benefit of 55 agent seconds in each instance. However,because only 1% of all calls fit this profile, the benefit relative toall calls is only 1% of 55 seconds, that is 0.6 seconds. With standardbenefit assumptions, IVR system analysis estimates a total IVR systembenefit of 13.4 seconds of saved agent time, shown in the lowerright-most cell of FIG. 8.

Routing a caller to the right destination is an important task of an IVRsystem, in view of maximizing both cost effectiveness and usability. Ifa caller is routed to the wrong agent, who then transfers the caller toanother agent, both agent and caller time are wasted. This subsectionpresents a method to quantify the effect of misrouting on costeffectiveness. To account for misrouting, calculation of total IVRsystem benefit is modified as follows. First, routing accuracy ismeasured for each distinct agent category, based on transcriptionanalysis. Routing accuracy is defined as the percentage of callers thatwere routed to the correct agent, relative to all callers that wererouted to that particular agent category. Then, the count for eachapplicable call profile is discounted by the rate of misrouting (whichis the complement of routing accuracy). The counts thus subtracted areassigned to new call profiles representing misrouting.

In the estimation of total IVR system benefit, misrouting receives apenalty of agent seconds by assigning a negative “benefit” to routing inthe automation category, as shown in FIG. 8. This penalty corresponds tothe time that the first agent spends talking with a caller untilrealizing that the call has to be transferred to another agent.

The example in FIG. 8 includes two misrouting profiles (“Misrouted tospecialist”), one with and the other without the benefit of calleridentification. In this example, misrouting reduces the total IVR systembenefit by 2.8 agent seconds, a figure calculated by adding the average“benefits” for the two misrouting profiles.

FIG. 10

In addition to the above-described automation categories, A, R, and I,it is particularly advantageous to consider “transactions” as anadditional automation category. An automation analysis may thereforeinclude the following four broad automation categories: customer ID,which is referred to as A in FIG. 10, routing (R), info delivery (I),and transactions (T). Also, estimates for how much agent time eachautomation category saves can be obtained using any common sense method,in addition to the two methods mentioned herein. An example of such acommon sense method is timing one's self in relation to how much timewould be spent in performing the corresponding task, for example askingsomeone for their telephone number. Thus, there are three methods forestimating the agent time saved for each automation category (benefitassumption). Namely, the agent time saved may be based on:

-   -   1. time-stamped annotations in agent-caller dialogs;    -   2. standard benefit assumptions; and    -   3. any other common sense method.

A technique for measuring the potential for increasing automation isdescribed next. The potential for improvement is defined as thedifference between the upper bound of automation for each automationcategory and the automation achieved in the existing IVR system.Although the automation actually achieved may be determined using themethod described above, two methods effective to determine the upperbounds on automation are described in the following paragraphs. Also,because automation may be quantified as a total IVR system benefit,i.e., the total agent-time saved through automation, the potential forincreasing automation can be translated into a potential for savingagent time and thus operational cost of a call center. Note that theexamples below do not refer to the same hypothetical call center thatwas used as the examples above. Therefore, the automation rates in theexamples below do not match the ones calculated there.

The first method measures upper bounds on automation based onannotations of automatable transactions in caller-agent dialogs. Thismethod will be described with reference to FIG. 10. In a specific formof annotation, transactions that can be automated in the existing IVRsystem and transactions that can be automated in a modified IVR systemare marked and time-stamped in caller-agent dialogs of a few hundredcalls, as described also in the discussion of mini assessment 5 above.Examples of such automatable transactions include: providing the agentwith the caller's account number, reading the account balance to thecaller, and scheduling an appointment for a technician to come to thecaller's house; other examples are shown in the column “automatabletransactions” in FIG. 10.

Once such transactions have been annotated, their frequency and averagehandling time can be estimated. The product of frequency and averagehandling time yields the savings potential for a specific transaction,expressed in agent seconds. The total opportunity is calculated byadding up savings potentials across all automatable transactions, andmultiplying the total by the percentage of calls that are handled byagents.

FIG. 10 lists automatable transactions in the left column and then crossreferences those transactions based on, for each automatable transaction(defining rows), the number of occurrences, the time spent, thefrequency, the savings potential, and the automation category (A, R, I,and T). For example, the savings potential for making a new paymentarrangement, relative to all calls handled by an agent, is 20.94seconds×13.7%=2.9 seconds. The total savings potential is the sum of thesavings potential entries for each automatable transaction. Thisopportunity is relative to all calls that are handled by an agent. Therate of calls handled by an agent, 72% in FIG. 10, can be obtained fromthe gathered IVR event-sequence data. An IVR event sequence, asdiscussed above, indicates, for each call, whether the call was enabledin the IVR system or was transferred to an agent. A weighted opportunityof 16.6 is relative to all calls. It is calculated by multiplying theopportunity relative to all calls handled by an agent (23.1 agentseconds in the example) by the percentage of calls handled by an agent(72% in the example). The numbers in the savings potential column are,for each automatable transaction, the product of “time spent” (averagetime spent by agents on handling the transactions) and frequency. Inthis example, the total potential for increasing automation, thus savedagent time, is determined to be 16.6 agent seconds relative to allcalls. A normalization relative to all calls facilitates translation ofthese savings of agent time into agent labor savings. The number ofagent seconds is simply multiplied by the total call volume into thecall center, which is readily available from standard call centerreports, and the agent cost per second.

FIG. 11

FIG. 11 illustrates a second method for determining upper bounds onautomation. This method is less accurate, but also much less costly.Instead of requiring annotation of all automatable transactions thatoccur in hundreds of agent-caller dialogs, which may require significanteffort if annotation is performed manually, this method requires onlyknowledge of the call-type distribution and how an IVR system couldbenefit each of the call types.

The call-type distribution, shown in the first two columns in FIG. 11,is estimated by annotating the call types of a few hundred calls thatwere handled by an agent, using one of the annotation methods describedin relation to the technique for call annotation 7, discussed above. Toestimate upper bounds on automation based on the call-type distribution,for each call type, which are shown as rows in FIG. 11, the automationcategories that are required to handle calls for each call type areindicated. In FIG. 11, a mark (x) in one of the automation categorycolumns indicates that a specific call type benefits from a certainautomation category. For example, callers of the type “balance/billing”would benefit from providing their customer ID, making the right menuselections to get to the balance/billing section (therefore an X in therouting column), and obtaining their account balance (therefore an X inthe info delivery column). Once this table has been filled out, theupper bound for each automation category is simply the sum of allcall-type frequencies (shown in the column labeled “% Calls”) where theautomation category column is marked. In this example, the upper boundfor customer ID is 95.7%, 71.5% for routing and information delivery,and 29.5% for transactions.

FIGS. 12 and 13

To determine the potential for increasing automation, the upper boundson automation are compared with the automation rates achieved in theexisting IVR system, as illustrated in the bar chart of FIG. 12. Theexisting automation rates were measured using the automation analysismethod described above.

As above, potential for operational savings is quantified as savings inagent time, expressed in agent seconds per call. To transform thepotential for increasing automation rates into a potential for realizingoperational savings, the automation rates are multiplied with thecorresponding benefit assumption, the agent time saved. The bottom partof FIG. 11 shows this calculation. Once automation rates have thus beenconverted into estimates for saving agent time, the potential for savingagent time is the difference between the upper bound and the savingsrealized by the existing IVR system, as illustrated in FIG. 13.

The output of the first method can also be visualized in the form shownin FIG. 13. To this end, the savings potential for each automationcategory is calculated by adding up savings potentials for alltransactions that are assigned to that automation category, as shown inthe next to last column in FIG. 10.

It should be noted that the second method yields much more optimisticupper bounds on automation than the first method described above,because it assumes that every call of a specific call type wouldactually benefit from a certain type of automation. However, in realityit is never true that all callers of a specific call type can actuallyget their problem solved in an automated (IVR) system, even though theservice needed is offered in the IVR system., Therefore, when trying toglean a realistic estimate for how much automation could actually berealized, upper bounds derived using the second method must bediscounted more heavily than upper bounds derived using the firstmethod. For the call center used as example above, the first methodestimated the total upper bound at 30 agent seconds while the secondmethod arrived at 40 agent seconds. Note that the description of thefirst method did not include the potential for increasing automatedcapture of customer ID, which was quantified at 14 agent seconds, butwhich was not included in FIG. 10 to reduce complexity of the figure.

User-Path Diagrams 9

The following subsections introduce user-path diagrams, also referred toas state-transition diagrams, as an extremely useful tool for evaluatingusability of call center IVR systems. Following the description ofuser-path diagrams, a method is described for conducting timing analysesin telephone user interfaces.

A user-path diagram is used to visualize user behavior of calls placedinto a call center by representing the actions taken and the finalconclusion (level of success) reached for many calls. A user-pathdiagram is a state-transition diagram where states correspond to actionsor prompts in the IVR system, and arcs correspond to user responses oractions initiated by the IVR system. Both states and arcs (transitions)are annotated with the amount of user traffic that reaches a specificstate. The amount of traffic may be represented by a count number, andabsolute and/or relative percentages. Each state also shows how manycustomers (callers) left the IVR system at that specific state, alongwith the level of success (i.e., automation or completion of thecustomer's goals) and the IVR exit condition. Optionally, transitions inthe user-path diagram may be annotated with the modality that triggeredthe transition. For example, some transitions may occur after pressing“1” or “2”, while other transitions occur if the caller provides noinput (“timeout”) or speaks to the IVR system.

A user-path diagram can be obtained for touch-tone, speech-enabled, andmultimodal IVR systems. Also, a user-path diagram can be generated atvarious levels of abstraction of the call flow of a call center. A statein a user-path diagram may represent one specific prompt in the callflow, or a prompt with all reprompts and retries, or a completesubsection of the IVR system, representing many different prompts.

An exemplary user-path diagram is shown in FIG. 14. A user-path diagrameffectively shows user behavior in an IVR system by representing IVRevent-sequence data as a tree. The nodes of the tree correspond to IVRstates or prompts, arcs correspond to state transitions, and leavescorrespond to IVR exit conditions of calls. As is shown in FIG. 14, eachnode and leaf is marked with the count of calls that reached the node orleaf, and with two percentages: the percentage relative to all callsthat reached the parent of the node, indicated in italics, and theabsolute percentage, relative to all calls in the data set. In addition,although not shown in the figure, arcs may be marked with user (caller)input that causes the corresponding state transition, such as pressing acertain touch-tone button in response to a prompt. Each state also showshow many customers left the IVR system at that specific state and itsexit condition. Such exit conditions include, but are not limited to:“self-serve” (or full automation)—the call was resolved in the IVR; “toagent”—the call was transferred to a live agent; and “abandon”—thecaller hung up, either in the IVR system without obtaining any usefulinformation, or on hold before reaching a live agent. If a call centeroperates with distinct categories of agents, the “to agent” category istypically broken down or separated into various subcategories, eachrepresenting a distinct queue of specialist agents. In addition, thelevel of automation may be added to each of the agent categories. Suchannotations may include: “cold to the floor” (routed to default agentwithout obtaining any useful information in the IVR system), “to floorwith ID (routed to default agent after providing the caller ID in theIVR system), “to floor with readout” (routed to default agent afterobtaining useful information in the IVR system), or “to floor withtransaction”.

In the exemplary user-path diagram of FIG. 14, rectangular boxesrepresent one or more IVR states, arrows represent call traffic, andcircles indicate locations where calls leave the IVR system. Forexample, of the 4319 calls that this hypothetical data set contains, 234calls (or 5.4%) abandon at the greeting. At the opening menu, 311 calls(or 7.2% of total calls) are transferred to a “floor” agent, claimingthey want to establish a new account. At the same menu, 4.3% of all thecalls reach a “floor” agent for other reasons, and 1.1% abandon thecall. FIG. 15A illustrates how to read a user path diagram.

IVR systems can be very complex, thus also user-path diagrams. To copewith complex IVR systems, usability analysis based on user path diagramscan be conducted in a modular fashion by clustering IVR states. In suchclustered user-path diagrams, nodes may represent clusters of states.The counts for a clustered user path diagram are obtained by adding upcounts for all states in a cluster, and by eliminating loops, i.e.,transitions between two states that belong to the same cluster. Forexample, in FIG. 14, “ID Entry” and “ALT ID Entry” are state clustersrepresenting a call flow for entering account numbers in two differentways.

User-path diagrams can be generated from complete IVR event sequencesfor many calls using the following computer-based method. The call-flowlogic is modeled as a non-deterministic finite-state automaton, which isa standard modeling tool in the field of computer science. The startstate represents the first prompt in the IVR system, most often agreeting. Other states of the call-flow model represent all promptswhere a branching occurs in the call flow, be it triggered by some kindof user input or by internal processing. In addition, the IVR exitcondition is captured in a set of end states, representing a callerhangup, a transfer to a floor agent, and a transfer to a specialistagent. The transitions of the automaton represent transitions betweencall-flow states, which may be triggered by caller DTMF dtmf input,spoken caller input, or no input (timeout).

After modeling the IVR call-flow logic as a non-deterministicfinite-state automaton, the IVR event sequences are fed through thefinite state automaton, using a standard acceptance algorithm. Duringthis process, a two way matrix of several counters is filled as follows.For every state with at least one child (subsequent state), there arestate-transition counters and a counter for each distinct modality. Inaddition, for all exit states there are counters representing thevarious IVR exit conditions, separating the different levels ofautomation achieved.

FIGS. 15-16A

Once this two-way matrix of several counters has been filled, it can berepresented visually as a state-transition diagram using standardvisualization techniques. FIG. 15 illustrates the process for generatinguser-path diagrams. As shown in the figure, the first step S1001 isperformed on the basis of a database of IVR event sequences 500, acall-flow model 600, and a lookup table 700. The lookup table 700stores, for each applicable IVR event, the automation that was achieved.

At step S1001, for every IVR event sequence, a transition and modalitycounter is incremented for every state transition in the event sequence.Also, an appropriate exit condition counter at the IVR exit point,including the level of automation achieved, is incremented. At stepS1002 the state transition data is used to produce a user-path diagram800. FIG. 15A illustrates how to read a user path diagram.

Usability problems can be identified by inspecting user-path diagrams.Usability problems reveal themselves, for example, as parts of the treethat receive little or no caller traffic, or as states with high ratesof abandoned calls or transfers to an agent. In other words, a user-pathdiagram can be interpreted as a funnel. In this view, “leaks” in thefunnel indicate usability problems.

For example, in FIG. 14, the state cluster named “ALT ID Entry” receives9.6% of all calls, but 86% of these calls either are abandoned or aretransferred to a floor agent, and the account number is correctlyentered in only 14% of calls. Obviously, this part of the IVR system isineffective.

Clustered user path diagrams lend themselves to analyzing theeffectiveness of sections of an IVR system, e.g., the section thatidentifies the caller. It has been found by the inventors that thesuccess rate (or yield) of an IVR system on such IVR sections is usefulto analyze IVR system usability for specific tasks. The yield of an IVRsection is defined as the ratio of incoming to outgoing calls in thecluster that represents the IVR section. For example, the yield foridentifying the caller for the IVR shown in FIG. 14 is(2761+59)/3542=79.6%, by adding appropriate counts for two stateclusters “ID Entry” and “ALT ID”.

Routing Analysis 1

The performance of an IVR system with regard to call routing isimportant in assessing the IVR system. Routing performance can begraphically summarized in a confusion matrix. Such a matrix graphicallydisplays the routing decisions made by the IVR system in comparison withwhere the caller actually should have been routed, according to his orher reason for calling. This information, thus arranged, facilitatescalculation of the routing accuracy of the IVR system. The procedurefollowed to generate the information to be displayed in the matrix isdiscussed in detail below, after a discussion of the confusion matrixitself and what can be gleaned from it.

An exemplary confusion matrix is shown in FIG. 16. A confusion matrixuses the information gathered by the IVR system analysis 6, the promptinference 6, and the annotation 7 techniques and graphically comparesthe way the IVR system routed calls with what was actually intended bythe callers, that is, the true reason for calling, or “truth”.

In the confusion matrix shown in FIG. 16, the rows indicate thedestination to which the IVR system actually routed the calls in ahypothetical call center. In the hypothetical call center, the callswere routed either to Specialty 1, Specialty 2, Specialty 3, or to thefloor (non-specialist agents). The columns indicate to which specialtythe caller actually needed to be routed. Note that the numbers along thediagonal from upper left to lower right represent correctly routedcalls, i.e., 33 calls intended for Specialty 1 were actually routedthere, 24 calls intended for Specialty 2 were actually routed there, 8of the calls intended for Specialty 3 were actually routed there, and150 of the calls intended for the floor were actually routed there.

On the other hand, it can be seen, for example, from the matrix that 22of those calls routed to the floor actually should have been routed toSpecialty 1. The matrix also shows, for example, that the true topic(confirmed by interactions with agents) of 55 calls was thatcorresponding to Specialty 1. This topic represents 18% of topic volume(55/307×100). The information also indicates that 60% of the callsintended for Specialty 1 actually were routed there. Conversely, only51% of the calls routed to Specialty 1 actually should have been routedthere (33/65×100). It can also be seen, for example that 16 of the callsintended for Specialty 2 were routed to Specialty 1, 26 went to thefloor, while only 8 were correctly routed. This is extremely importantinformation in assessing an IVR system, because misrouting costs as muchas auto-routing saves, in that the wrong agent's time is wastedrerouting the call. The overall accuracy of the system can be calculated(in %) as the number of correctly routed calls (215, adding up thefigures in the diagonal) divided by the total number of calls (307) andmultiplied by 100. In the hypothetical, the overall accuracy is 70%.

To convert the end-to-end call-flow information gathered in the IVRlogging 3 and annotation 7 techniques into a form that may easily bedisplayed in the form of a confusion matrix requires manipulation ofseveral data files containing data gathered from end-to-end calls. Themethod of manipulating those files is described next with reference tothe flow chart of FIG. 16A.

First, at step S20A, the call flow of a particular IVR system issummarized in a specially formatted input file (.cfs file). The .cfsfile encapsulates a call-flow model from information provided by theorganization running the IVR system and by listening to and making callsto the IVR system. At step S20, calls are recorded end to end, asdiscussed in detail above. At step S21, agent-caller interaction iscoded and annotated. At step S22, event detection and prompt inferenceare performed, with reference to the .cfs file, and the IVR portion ofthe call is summarized in a .typ output file. The .typ files (one percall) are compiled at step S24 to create a summary (.sum) file of, amongother items, information collected from the caller in the IVR system andthe routing destination out of the IVR system for each call.

FIG. 17

FIG. 17 is an example of an excerpt from a .sum file. As can be seen,each line contains a summary of a particular call.

As has been discussed in detail previously, at step S21 of FIG. 16Atranscribers annotate the post-IVR portion of the call for calls thattransfer out of the IVR system, noting, among other things, when callstransfer to an agent (or end), topics discussed, information collected,and transactions completed for the customer (caller). Global informationabout the call, e.g., whether the customer was satisfied and whether theproblem resolved, may also be noted. The coding scheme is summarized, atstep S21A in a codebook input file (.cb file). (The transcriptionanalysis technique is discussed above.) Then, at step S23, summaryresults are created for the coded transcriptions. The coding scheme alsocombines the coded information for each call with that from the IVRsystem analysis for that call to generate a profile of how many callersare asked again for information that was requested and/or acquired inthe IVR system, and cross-tabulates the IVR system routing and thetopic(s) discussed in the agent-caller dialog to generate a confusionmatrix, as shown in FIG. 16A, at step S26.

The files discussed above are described in more detail as follows:

A .results file includes summary statistics for the coded agent-callertranscription; times to collect information from callers or provideinformation to them; in IVR, transfer, hold, and with-agent times;breakdowns of calls by any global information items collected; andbreakdowns of calls by first topic, any topic, topic list, call routing(topic) types, information provided or collected. The timing statisticsare further broken down by the routing type of the call.

A .summary file gives both IVR system analysis and with-agenttranscription results in spreadsheet form. Such files are discussed inmore detail below.

A confusion matrix file is a cross-tabulation of the calls routed by IVRsystem routing and the actual caller topic noted in the transcription ofthe agent-caller portion of the call.

There are three segments to most calls to an IVR system: the IVRportion; the hold portion; and the caller-agent interaction. To segmenttime by call type, the automation analysis tools, in addition tocreating the .sum file, as described above, also create a report (.rpt)file that includes summary statistics for time spent in the IVR systemuntil transfer or abandonment. The summary results created by thetranscription analysis techniques include statistics on the amount oftime spent in the IVR system, in the initial transfer to an agent, withan agent, and in subsequent transfers for all calls and broken down bycall (routing topic) type.

The information from the .rpt and .results files is then combined atstep S25 to profile calls, at step S27, by time spent in the IVR systemfor all calls, in the initial transfer out of the IVR system, for callsthat do not self-serve or are abandoned in the IVR system, and after anagent takes the call, for calls that transfer out of the IVR system anddo not hangup during the initial hold. Calls for which a call-routingtype can be identified have “subprofiles” by call type.

FIG. 18

This profile information, presented in both tabular and graphic(barchart) form, is called a life-of-call diagram, as shown in FIG. 18.The above process produces profile information for the segment times bycall type:

A .typ file is generated from client application logs, from simulatorlogs, or from prompt detection/inference. The format can be read by atranscriber's standard software tool, which allows caller-agentinteraction annotations to be added. The .typ file acts as therepository of information for an individual call, both from theautomated IVR system analysis and transcriber annotations, and acts asthe basis for analysis by parsing tools.

A .sum file is a single file for an entire set of data, where a set ofdata is whatever list of .typ files is selected for processing as a set.The .sum file consists of a one-line summary per file of major events ina call (prompts, DTMF tones, etc.) and the final information ondestination, information exchanged, and the manner of exit from the IVRsystem (e.g., to agent or hangup). The file acts as the basis forfurther “batch” analysis, such as analysis to create a user-path diagramand to merge information from IVR system analysis and caller-agentinteractions.

A .elog file consists of a single file for an entire set of data, wherea set of data is whatever list of .typ files is selected for processingas a set. The file has one “paragraph” per file of major events in thecall, including a list of prompts and responses, the timing of theprompts and responses, and various other “global” statistics for a call(e.g., number of tries for each piece of information, exit states, etc.)

A .rpt file includes counts and summary statistics computed across anentire set of data, where a set of data is whatever list of .typ filesis selected for processing as a set. This file is generated at the sametime as the .sum file, and includes timing statistics for variouscheckpoints in a call (time to transfer, time to first readout, etc.),as well as count breakdowns by various criteria, such as destination outof IVR system, number of prompts heard, information exchanged, topicsgiven in the IVR system (for speech applications), etc.

Inputs to an IVR system automation analysis tool include a .typ file anda .cfs file.

A .typ file, as described above, acts as the basis for analysis by anIVR system automation analysis tool. Events are noted in pairs of lines:a timestamp line, followed by the event(s) that occur at that time.Events include start of call, end of call, prompts, DTMF tones, speechin recognition, topic identification, and coded events as annotated bytranscribers. The coded events include agent greetings, holds,transactions, topics, and transcribed customer (caller) speech.Transcriber annotations that refer to the entire call (such as whetherthe customer issue was resolved, whether the customer was upset, etc.)can be encoded in start-of-call or end-of-call fields.

A .cfs file encapsulates a call-flow model in a specified format used byIVR system automation analysis tools. The .cfs file includes promptname, information collected or provided (readout) at the prompt,subsequent prompts, and caller actions that lead to each possiblesubsequent prompt.

The automation analysis tool discussed above takes a list of .typ filesand a .cfs file as input, and steps through each .typ file, parsing outevents and their associated times, and using information in the .cfsfile to deduce what information was collected, the final disposition(abandon, self-serve, to-agent, etc.) of the call, and the sequence ofevents. The event sequence is vetted to make sure that it is possibleunder the call flow as documented in the .cfs file. As each call isprocessed, counts and summary statistics are updated, and the entriesfor that call in the .sum and .elog files are generated.

Outputs from the IVR system automation analysis tool include a .sumfile, a .elog file, a .rpt file, all of which are described above. Theoutputs also include a .counts file and a .success file.

A .counts file includes a brief overview of counts and percentages ofcalls by disposition, full automation, transfer to agent, hangup,abandon, prompt detection, or simulator failure. This file can be usedto detect shifts in the success of the prompt detection or the simulatorthat creates the .typ files used for analysis, or in abandon andautomation rates.

A .success file provides, for each type of information prompted for inthe IVR system, counts and success rates for eliciting that informationfrom callers, broken down in various ways, such as by number of tries,by mode (DTMF, speech recognition, no response). For example, a successfile can give the number and percent of callers that provide theirtelephone number by speech at the first try.

The user-path diagrams discussed above are generated using the .sumfile, created by the IVR system automation analysis tool, and the .cfsfile. A matrix of from-to pairs is generated and updated for each callby parsing the event sequence for that call as given in the .sum file.From this information, a .trans file is created, which reports the totalnumber of calls that transited from state A (usually a prompt, start, orend state) to state B for each from-to pair observed. When the “to”state is an exit condition (transfer to agent or hangup in the IVRsystem), the exit information from the .sum file is preserved in the.trans file.

A .trans file gives the counts of calls that transit from state A tostate B, along with a breakdown of those counts by mode (DTMF, speech,or silence), and is used as the input to tools for generating the actualuser-path diagram (graph), and to roll up or agglomerate associatedstates (e.g., a prompt and immediate retries) into a single state forgraphing and analysis purposes.

A transcription analysis tool that analyzes transcriber (annotator)codes and annotations takes as inputs a list of .typ files and a .cb(codebook) file that gives the annotators' codes, their names, and topicand routing groupings, if applicable, e.g., allowing all billing-relatedtopics to be tracked both individually, and collectively as “billing”.Optionally, a .sum file created by an IVR system analysis tool can beinputted, in which case the transcription analysis tool will also mergeinformation from an IVR system analysis. For each call (.typ file)listed, the transcription analysis tool parses the events and associatedcodes in the .typ file. As each call is processed, counts and summarystatistics are updated, and the entry for that call in the .summary fileis generated.

Outputs of the transcription analysis tool include a .summary file and a.results file.

A .summary file gives information for each call (timing statistics,topics noted, information exchanged, etc.), at one line per call, in aformat that can be easily read into a spreadsheet program or parsed byother analysis tools.

A .results file, as discussed above, includes counts and summarystatistics computed across an entire set of data, where a set of data iswhatever list of .typ files is selected for processing as a set. The.results file is generated at the same time as the .summary file, andincludes timing statistics for various checkpoints in the call, time toexchange each type of information, time with agent(s), as well as countbreakdowns by various partitions, such as first topic, any topic,information exchanged, number of topics, number of agents, type ofrouting agent pool of topic, etc.

If a .sum file from an IVR system automation analysis is one of theinputs to the transcription analysis tool, the .results file that isgenerated will include merged statistics on incidences of callers beingqueried by an agent for information they have already been asked forand/or provided in the IVR system.

Sections of a .results file that give call segment times (time in IVRportion, time on hold to agent, time with agent) broken down by routingtype, and timing statistics for calls that are abandoned while on holdfor an agent, are combined with information from the .rpt file createdby the IVR system automation analysis tool to generate information ontime segmentation by call type, i.e., a life-of-call diagram, which isdiscussed next.

Life-of-Call Diagram 10

To conduct timing analyses of telephone user interfaces, completiontimes are measured based on call-event traces, which contain time stampsfor every significant event in a call. Timing analyses can be conductedat various levels of detail, ranging from timing broad sections of acall, such as IVR, on hold, and agent-caller dialog, to measuring taskcompletion times for specific tasks, such as entering an account number.

Useful insights about caller experience within a telephone userinterface can be gained by tabulating the timing of broad sections fordifferent call profiles, which will be referred to as a life-of-calldiagram. At least the following two profiles should be considered:abandoned calls and calls that were transferred to an agent.

More profiles should be considered if calls can be classified intodistinct service categories. Based on annotations that characterize thereason for a call, obtained from transcription analysis, life-of-calldiagrams graphically represent user (caller) experience across theservice categories. For example, the life-of-call diagram in FIG. 18shows four different agent categories A-D. It can be seen that the callcenter services calls of type C much faster than type A. The profile“Hangup during transfer from IVR” shows that callers' patience during atransfer is typically exhausted within 90 seconds of waiting on holdwhile being transferred.

Beyond evaluation, the assessment analyses techniques of the presentinvention also provide guidance for improving interface design. Thefollowing outlines how to apply these techniques to the design orredesign of telephone user interfaces.

IVR system automation analysis and calculation of a total IVR systembenefit discussed above, can be applied to quantitatively comparealternative IVR system designs. For a comparative IVR system analysis,automation rates are calculated separately for both IVR systems, in themanner presented above. Differences in automation rates indicate whichIVR system design is better, separately for each task (automationcategory). For a decision based on a single number, the difference intotal IVR system benefit reveals which design is superior overall.

Comparative IVR system analysis can thus validate that a new IVR systemdesign is indeed better, and furthermore, it quantifies the costsavings. FIG. 19 compares three IVR system designs: the original designand two new designs (an initial touch-tone redesign and a finaltouch-tone and speech-enabled design). The height of the columnsindicates the total IVR system benefit.

It can be seen that the initial touch-tone redesign increased total IVRsystem benefit from 17 to 26 agent seconds. By comparing threeautomation categories “account number (entry)”, “information delivery”,and “routing to specialists, it can be seen that this increase is due toimproved capture of account number and information delivery. Hence, thiscomparative IVR system analysis validates that the touch-tone redesignis indeed superior to the original system, and quantifies the savings as9 agent seconds. In this call center, with 15 million calls per year andan agent cost-per-minute of 52 cents, this corresponds to annual savingsof more than $1 million.

IVR system automation analysis and benefit calculations can provide thenecessary business justification of IVR system redesign, because thecost savings of a redesigned IVR system can be estimated. Based on anautomation analysis of an existing IVR system and knowledge of usabilityproblems, bounds for improvements in the various automation categoriescan be derived. Given such bounds, the total IVR system benefit can beprojected to calculate bounds on the annual cost savings.

FIGS. 19 and 19A

In FIG. 19, for example, the numbers for touch-tone and speech-enableddesign are based on such benefit projections. The projected increase intotal IVR benefit to 34 agent seconds corresponds to annual savings ofmore than $400,000. For the call center at hand, this providesjustification to implement the speech-enabled design, which is expectedto greatly improve usability and user satisfaction, in addition to theprojected cost savings.

Given an assessment of automation potential, techniques of the presentinvention can estimate how much of an opportunity for savings can beexpected to be realized by redesigning an IVR system. Based on expectedsavings in agent time per call, hourly rates for agents, call volume,and anticipated costs for implementing changes, a comparative businesscase can be built for touch-tone IVR system re-engineering versusspeech-enabling a call center's IVR system. FIG. 19A illustrates theprojected benefits for a touch-tone redesign versus a speech-enableddesign in a case study of a customer care center of a largetelecommunication service provider handling 10 million calls per year.Savings for a fully automated system is labeled as maximum automationpotential, which can never be fully realized. The results show thatwhile $1.2 M can be saved through touch-tone re-engineering, aspeech-enabled interface can provide an additional $1 M in savings, fora total of $2.2 M in savings. Typically, a call center will implementthe touch-tone redesign while proceeding with development of thespeech-enabled interface, offsetting the cost of the speechimplementation with the money saved from the touch-tone redesign.

If a decision is made to go ahead with speech technology, the first stepis to simulate a speech-enabled interface and present it to livecallers. This enables speech data needed for configuring the speechrecognizer and natural language processing technology to be collected.The results are used to optimize a speech-enabled IVR system prior toevaluating a prototype on a larger audience using thousands of calls. Tomeasure the actual performance of the speech-enabled IVR system, thesame methodology used during assessment of the initial IVR system isapplied. Thus, the actual benefit delivered by the speech-enabledinterface can be accurately measured before the speech-enabled IVRsystem is rolled out in production mode. This phased-approach postponesthe investment required for hardware and software until the benefit ofspeech is proven.

IVR Performance Monitoring 4

FIG. 20

Another method of monitoring the performance of an IVR system involvesthe use of data logging and analysis to provide operational monitoringof IVR system performance and, in particular, performance on truebenefit measures, such as agent-time savings, as defined in automationassessment discussed above. IVR system performance monitoring 4addresses the problem of deciding when to tune up” an IVR application torecover performance that may have degraded due to changing conditions ina call center. In IVR system performance monitoring 4, which isillustrated in the diagram of FIG. 20, an IVR application may usetouch-tone or speech interfaces. Performance is defined in terms of“true” benefits, such as calls routed to the correct destination, orcustomer ID numbers captured correctly. In day-to-day operation, it iseasy to collect information from the IVR system or caller-agent dialogue(CAD) about how calls are handled, but it can be difficult or impossibleto obtain “truth”, that is, what the caller really intended, in order todetermine whether calls are processed correctly.

In one embodiment of the invention, as shown in FIG. 20, a data analysisstep S2000, as shown in the figure, utilizes IVR logs, CAD reports, and“truth” data from an IVR unit 900 to determine performance. “Truth” maybe determined directly, for instance in the IVR system by asking callersto indicate correctness, by querying a sample of callers in the IVRsystem for truth, or in an ACD call routing unit 902 by 1 agents 903 whoindicate correctness either directly with correctness judgments orindirectly by their handling of the call. For instance, agents whoreroute calls 904 to another agent skill group indicate by their actionsthat the call must have been misrouted in the first place. Similarly, ifagents 903 enter a customer ID different from that collected by the IVRsystem, then the IVR system must have been incorrect. For such a systemin which truth is available, the data analysis step S2000 involves amatching of truth against call handling data available from ACD reportsand IVR logs. Performance measures calculated by running assessment datathrough a performance model 905, such as correct routing and correctcustomer ID entry percentages, can be calculated and reported directly.If performance falls below some selected threshold, then an alarm can beraised. The data analysis step S2000 then generates an IVR systemperformance report 906 and, if performance has degraded below apredetermined threshold, a performance degradation alarm 907 may be setoff.

When the truth is not available, performance and performance degradationmay still be inferred from measurable quantities that are correlatedwith truth-based measures. Correlated, measurable quantities may includedata such as the following:

-   -   percentage of calls routed successfully (not necessarily        correctly) to agents or self-service;    -   rate of information delivery;    -   rate of completion for available automated transactions; and    -   rate of abandon without information.

For systems based on speech recognition and natural language processing,the following may also be included:

-   -   rate of rejection, where the system cannot match any recognition        text or topic to what is spoken; and    -   average confidence or output score for recognition or topic ID.

As described above, the data analysis step S2000 for the preferredembodiment of IVR system performance monitoring uses, as input, logsfrom the IVR logging process, ACD reports, and the true call type, asdetermined by the agents for calls handled by live agents, and asdetermined by querying a small percent of callers that were handled byautomated fulfillment. Note that callers served by automated fulfillmentdo not have to be queried constantly, rather, estimates can be derivedby sampling a few hundred calls and updating these estimates asnecessary.

An IVR system performance report 906 is preferably a report configuredto show current values for several parameters that correlate with IVRsystem benefit. Examples of such parameters include rates of transactioncompletion, information delivery, abandon without information, andsuccessful routing. These parameters are measured automatically asfollows. Complete IVR event sequences are extracted from the logs, asdescribed above in relation to the IVR logging 3 technique. Based on theextracted IVR event sequences, rates for different call profiles can beestimated using the method for measuring existing automation rates,which is described in the above discussion of automation analysis 8. Inparticular, the rates for abandoning without information, and the totalautomation achieved on delivering information and completingtransactions is thus obtained. Finally, to measure the number and rateof successful routing, an IVR system routing analysis is performed usingthe IVR exit point from the event sequence and knowledge of the truecall-types for calls during an appropriate sampling period. Acomputer-based method for performing a routing analysis is describedabove in relation to FIG. 16A.

A performance degradation alarm 907, shown in FIG. 20, is issuedwhenever at least one of the above parameters falls below a certainthreshold. These thresholds are determined by competent call centerpersonnel when configuring the IVR system performance monitor.

Telephone interfaces, an important class of human-computer interfaces,have been neglected by researchers in the field of human-computerinteraction. Usability evaluation and engineering methods for telephoneinterfaces are not well developed. Decision makers in call centers yieldto strong financial pressures, striving to cut costs without being ableto assess the significant impact of usability on customer satisfactionand the financial bottom line. To remedy this situation, the presentinvention presents an assessment methodology for telephone userinterfaces that evaluates both cost effectiveness and usability. Movingbeyond previous laboratory studies of research spoken dialog systems,which evaluate only task completion rate and time, the methodology ofthe present invention allows practitioners to evaluate usability oftelephone interfaces in a systematic and comprehensive fashion.Furthermore, this methodology can be applied to production call centersthat service millions of calls per year.

An evaluation of telephone user interface must be based on thousands ofend-to-end calls. Calls are recorded or observed in their entirety tocapture the complete user (caller) experience, and often thousands ofcalls are necessary to obtain statistical significance in the analyses.Methods to analyze such large amounts of audio data efficiently havebeen presented above. IVR system analysis, as described above,transforms Gigabytes of audio data into complete event traces. For theIVR portion of a call, the event sequence is captured in a fullyautomated procedure, while manual transcription, or audio miningtechniques, are used to annotate events in agent-caller dialogs. Theassessment methodology of the present invention is applicable not onlyto commercial, but also to research telephone interfaces.

The above example embodiments of the assessment techniques of thepresent invention are illustrative in nature, and do not limit the scopeof the invention, which is defined by the appended claims.

1. A method for forming a database indexed with content-based parameterscorresponding to recordings of contacts to a contact center, the contactcenter being operable to present a contacting party with a contact thatincludes an automated portion corresponding to interaction with anautomated response interface and, at the contacting party's option, anagent portion corresponding to interaction with an agent, eventsoccurring in the recordings of automated portions being ascertained bypreviously executed automated-portion analysis techniques, said methodcomprising the steps of: selecting content-based parameters to be usedas indices in a database; analyzing results of the automated-portionanalysis techniques to detect occurrence of events corresponding to theselected parameters; analyzing one or more agent portions of recordingsof contacts to ascertain events occurring therein; correlating eventsoccurring in the one or more agent portions of the recordings withcorresponding ones of the selected parameters; and making entries in thedatabase based on the selected parameters and the correlated events. 2.A method according to claim 1, wherein the contact center is a callprocessing center, the contacts to the contact center are calls to thecall processing center, and the automated response interface comprisesan interactive voice response (IVR) unit.
 3. A method according to claim2, wherein the content-based parameters include parameters correspondingto semantic annotations, which include annotations corresponding tocharacteristics of a call, a content of the call, and a quality of thecall.
 4. A method according to claim 1, wherein the content-basedparameters include at least one of a mention of an entity name, acompletion of a specified transaction, a request for a specifiedtransaction, and a request for a specified service.
 5. A system forforming a database indexed with content-based parameters correspondingto recordings of contacts to a contact center, the contact center beingoperable to present a contacting party with a contact that includes anautomated portion corresponding to interaction with an automatedresponse interface and, at the contacting party's option, an agentportion corresponding to interaction with an agent, said systemcomprising: analysis means operable to perform automated-portionanalysis techniques to ascertain predetermined events occurring inrecordings of automated portions of contacts to a contact center; miningmeans operable to perform audio mining of recordings of agent portionsof the contacts to the contact center, said mining means including: aspeech/non-speech detector operable to identify speech and non-speechevents in an audio recording; a speaker-change detector operable toidentify speaker turns in an audio recording; a speech recognizeroperable to output a sequence of words for each speaker turn identifiedby the speaker-change detector; a topic detector operable to determine atopic of an audio recording; and a named-entity detector operable toidentify speech pertaining to an entity named in an audio recording; andcorrelation means operable to correlate an event occurring in an agentportion of the recordings with a corresponding one of a plurality ofcontent-based parameters selected to be used as indices of a database,and to make an entry in the database based on the correlated event andthe corresponding content-based parameter.
 6. A system according toclaim 5, wherein the contact center is a call processing center, thecontacts are calls to the call processing center, and the automatedresponse interface comprises an interactive voice response (IVR) unit.7. A system according to claim 6, wherein the plurality of content-basedparameters include parameters corresponding to semantic annotations,which include annotations corresponding to characteristics of the call,a content of the call, and a quality of the call.
 8. A system accordingto claim 5, wherein the content-based parameters include at least one ofa mention of an entity name, a completion of a specified transaction, arequest for a specified transaction, and a request for a specifiedservice.
 9. A computer program product embodying a program forimplementing a method for forming a database indexed with content-basedparameters corresponding to recordings of contacts to a contact center,the contact center being operable to present a contacting party with acontact that includes an automated portion corresponding to interactionwith an automated response interface and, at the contacting party'soption, an agent portion corresponding to interaction with an agent,events occurring in the recordings of automated portions beingascertained by previously executed automated-portion analysistechniques, said computer program product comprising code for: selectingcontent-based parameters to be used as indices in a database; analyzingresults of the automated-portion analysis techniques to detectoccurrence of events corresponding to the selected parameters; analyzingone or more agent portions of recordings of contacts to ascertain eventsoccurring therein; correlating events occurring in the one or more agentportions of the recordings with corresponding ones of the selectedparameters; and making entries in the database based on the selectedparameters and the correlated events.
 10. A computer program productaccording to claim 9, wherein the contact center is a call processingcenter, a contact is a call to the call processing center, and theautomated response interface comprises an interactive voice response(IVR) unit.
 11. A computer program product according to claim 10,wherein the content-based parameters include parameters corresponding tosemantic annotations, which include annotations corresponding tocharacteristics of a call, a content of the call, and a quality of thecall.
 12. A computer program product according to claim 9, wherein thecontent-based parameters include at least one of a mention of an entityname, a completion of a specified transaction, a request for a specifiedtransaction, and a request for a specified service.